Tech Industry Job Roles

Around March, I was tasked to Hire 2 DevOps Engineers for a start-up company in Kolkata. During lunch with the Founder, he casually asked: "Sandip da, what do you generally look into when Hiring DevOps Engineers?" I said: "I look for traits such as being generalists, tinkerers, and “glue people” who integrate various system components seamlessly." What did I mean by this?: 𝐆𝐞𝐧𝐞𝐫𝐚𝐥𝐢𝐬𝐭𝐬: Versatile professionals who can handle a wide array of tools and technologies, adapting swiftly to the ever-evolving landscape. 𝐓𝐢𝐧𝐤𝐞𝐫𝐞𝐫𝐬: Individuals driven by curiosity and a passion for continuous learning, always exploring new ways to optimize and innovate. 𝐆𝐥𝐮𝐞 𝐏𝐞𝐨𝐩𝐥𝐞: Experts at integrating various system components, ensuring everything works seamlessly together. Let's be honest here and have a look at the below list of tech tools commonly used by DevOps Engineers in their Day-to-Day work life: Version Control: Git, GitHub, GitLab, Bitbucket CI/CD: Jenkins, CircleCI, Travis CI, GitLab CI, Bamboo, TeamCity Configuration Management: Ansible, Puppet, Chef, SaltStack Containerization: Docker, Podman Orchestration: Kubernetes, Docker Swarm, Apache Mesos, Nomad Cloud Providers: AWS, Google Cloud Platform (GCP), Microsoft Azure, IBM Cloud, Oracle Cloud Infrastructure as Code (IaC): Terraform, AWS CloudFormation, Azure Resource Manager (ARM), Pulumi Monitoring and Logging: Prometheus, Grafana, ELK Stack (Elasticsearch, Logstash, Kibana), Splunk, Nagios, Zabbix, Datadog, New Relic Automation and Scripting: Bash, PowerShell, Python, Ruby, Go Security and Compliance: HashiCorp Vault, AWS IAM, Aqua Security, Snyk, Clair Collaboration and Communication: Slack, Microsoft Teams, Jira, Confluence, Trello Build Tools: Maven, Gradle, Ant, Make Artifact Management: JFrog Artifactory, Nexus Re WOW, As the industry rapidly evolves, so do the tools and technologies we utilize. To keep pace, a DevOps Engineer must be a generalist, continuously learning and adapting while excelling at system integration. In your career, focus on being versatile, curious, and integrative. These qualities will set you apart and ensure your success in the dynamic field of DevOps. Cheers, Sandip Das #DevOps #TechHiring #CareerGrowth #ContinuousLearning

Ever wondered how a real AI project actually works ? A successful AI project goes through 7 structured steps, each led by different experts. From defining the business problem to continuous improvement after deployment, every role plays a part in making AI work in the real world. Here’s a cheat sheet that breaks down the end-to-end AI project lifecycle with clear steps, leaders, and responsibilities. ✅ AI Project Steps Covered: 🔹Step 1: Defining the Problem → Led by business analysts & product managers. Identify real problems, set objectives, align business & tech needs. 🔹Step 2: Preparing the Data → Led by data engineers & analysts. Collect raw data, clean, standardize, and split into training, validation, and test sets. 🔹Step 3: Building the Model → Led by ML engineers & data scientists. Choose algorithms, engineer features, train models, tune hyperparameters, and compare best fits. 🔹Step 4: Testing & Evaluation → Led by data scientists & ML researchers. Validate with unseen data, use metrics (accuracy, recall, AUC), stress-test, and decide if model is production-ready. 🔹Step 5: Deployment → Led by MLOps engineers & software developers. Package models into APIs, use Docker/Kubernetes, integrate with apps, enable predictions, and ensure reliability before going live. 🔹Step 6: Validation & Monitoring → Led by validators, ethicists, QA teams. Monitor accuracy, detect drift, check bias, log failures, and trigger alerts if performance drops. 🔹Step 7: Continuous Improvement → Led by data scientists, PMs, domain experts. Gather feedback, add new data sources, retrain, optimize pipelines, and push regular updates. Save this guide and share with others, and hopefully this will help to understand how AI projects work, step by step, role by role! #AI

AI-ML Lifecycle and Key Job Roles   In this age where we see more and more potential of how artificial intelligence and machine learning (AI/ML) can revolutionize industries and processes. Hence it is critical that everyone be aware of the AI-ML lifecycle and the job roles associated with it. Today, we will discuss the essential roles from conception to completion.   1. Domain Expert & Product Owner - Navigators   AI/ML projects start with SMEs and business experts. They identify gaps and opportunities and link company strategy with data-centric goals. Their subject expertise and business insights guide the AI/ML journey.   2. Data Engineer - Data Alchemists   Data preparation follows business needs. Data engineers design the systems that store, manage, and gather all this data. They format, wrangle, and pre-process data to prepare it for analysis.   3. Data Scientists - Insight Miners   AI/ML data scientists investigate and discover. They use statistical and ML algorithms to find patterns, insights, and prediction models in pre-processed data. They continually modify their models to transform raw data into value.   4. Machine Learning Architects - Blueprint Designers   AI/ML ecosystem strategists are ML architects. They choose methods, features, and data sets for ML solutions. They collaborate with ML engineers and data scientists to guarantee model translation into production, scalability, reliability, and performance.   5. Machine Learning Engineers: Bridge-Builders   Data science meets software engineering in ML engineers. They design, optimize, and turn ML models into robust, scalable, and efficient software solutions. They develop systems to handle real-time data, integrate the model into operations, and solve latency concerns.   6. DevOps Engineers and Technical Architects - Strategists & Implementers   Finally, DevOps engineers and technical architects implement AI/ML models. Deployment, monitoring, security, and scalability depend on them. They employ innovative technologies to track model performance and change depending on real-world feedback to ensure long-term success and business alignment.   The AI/ML lifecycle is a well-orchestrated symphony of roles. Each player, from SMEs establishing the direction to DevOps engineers implementing and monitoring the models, is crucial. This process goes well with my favorite quote "All of us are smarter than one of us"   Understanding this dynamics, make AI/ML projects effective and impactful. How does your company manage the AI/ML lifecycle? Are there any additional responsibilities that you consider essential to this journey?   Reference: Deloitte, Nasscom, McKinsey, PwC

In-house lawyers who wait to be invited into the conversation are already too late. The ones who make an impact embed early—and understand the business at the system level. Not just “we support product,” but: -Knowing how Salesforce tracks deals, and how legal terms (data use limits, indemnities) fit directly into CPQ workflows. -Understanding Jira structures—so a “small feature update” does not turn into a major privacy risk. -Tracking code freezes and release branches in GitHub to time approvals with development, not after. -Seeing how Zendesk ticket macros shape liability exposure before complaints escalate. -Knowing how marketing teams manage consent flows in HubSpot, Segment, or Amplitude—because compliance is built there, not drafted later. It is not enough to be “proactive.” You need to know where risks are born—inside the systems and workflows that drive the business: -Joining biweekly product demos, not just launch meetings. -Attending sales enablement sessions to hear real friction points, not just legal summaries. Building launch checklists that catch legal risks while there is still time to fix them. Lawyers who do this are not “legal checkpoints. They are part of how the company scales, safely and fast. It is about building business fluency to catch risks earlier, shape better decisions, and help the company move. #legaltech #innovation #law #business #learning

Prompt engineering ≠ typing good English Get it wrong and it can break your business I've lost count of how many times I hear: "It's just writing clever instructions" or "You must be ex-OpenAI to do prompt engineering" But real prompt engineering is much more than that. Here is what it actually takes: → Industry standard benchmarking → Legal compliance coordination → Security vulnerability testing → Prompt injection prevention → Safety filter implementation → Multi-step workflow design → Few-shot example libraries → Rate limiting configuration → Conversation log analysis → Conditional logic creation → Token cost optimization → Version control systems → Audit demographic bias → Edge case debugging → User intent mapping → Build testing suites → A/B test execution → API integration testing → Model drift monitoring → Chain-of-thought flows → Team training facilitation → Context window optimization → Fallback mechanism building → Model fine-tuning coordination → Output format standardization → Prompt caching implementation → Design decision documentation → Business requirement translation → Cross-model compatibility testing → Performance monitoring automation → Production deployment orchestration → Stakeholder expectation management Most of this work isn't about crafting clever instructions (though that's part of it). Prompt engineering is invisible until it goes wrong. When done well, the AI "just works." When done poorly? You're looking at hallucinations, bias, security vulnerabilities, and million-dollar failures. Here's the real secret: If you can master this chaos, you become indispensable. You are not just a prompt engineer. You're pure gold. 💭 What's your take? Are you a prompt engineer dealing with these challenges, or do you still think it's "just good communication skills"? ♻️ Repost to help your network achieve success. And follow Basia Kubicka for more.

Prompt Engineering is not dead, it is disappearing… into something bigger! For a time, prompting was seen as a tactical skill: how to ask better questions, structure inputs, and guide models. That era is already behind us. Today, prompt engineering is being absorbed into the design of intelligent systems. We are moving: - From prompts → systems - From instructions → context architecture - From outputs → orchestration of workflows This is the rise of: ✔️ Agentic prompting → where AI executes, not just responds ✔️ Context engineering → where intelligence is shaped by environment, memory, and data ✔️ Pull prompting → where systems retrieve what they need, when they need it ✔️ Systems prompting → where behavior is governed, not just instructed In this new reality, prompting is no longer about interaction, it is about architecting Intelligence. Even leading frameworks, such as those emerging from Harvard, are converging on three deeper capabilities: Intent precision → defining the real job to be done Context architecture → grounding intelligence in reality Judgment & verification → validating outputs in high-stakes environments Here is the critical shift: as AI systems become more autonomous, prompting is no longer just a technical skill; it becomes a matter of governance, control, and trust. The future will not belong to those who prompt better; it will belong to those who provide better context and design how intelligence works. #AI #AgenticAI #PromptEngineering #DigitalTransformation #ResponsibleAI #FutureOfWork

What roles turn a legacy technical team into an AI team that’s ready to deliver value vs. endless PoCs? Just as the AI stack must prioritize value over hype, the AI team’s composition must realign to deliver growth. Data analysts make excellent decision analysts. The focus moves from reporting (BI) with no value to outcomes (AI) with high business and customer impact. Why do business users need data? What outcome or customer value are they trying to deliver? The transition to decision analytics puts the data analyst’s technical skills in line with their business and domain expertise. The result is a high-value role. Data and BI engineers are in the best position to support the business’s emerging information needs. High-value AI is an information product. Decision-makers need information to improve outcomes and create value more efficiently. ML engineers and data scientists have AI engineering skills, so the major shift happening here is from PoCs to products. The product-first mindset and skillset are critical to support AI teams that directly impact the top and bottom line. Product owners and PMs are becoming product strategists and value owners. They ensure that the AI team only works on projects with significant ROI. They shield the AI team from endless PoCs by supporting opportunity discovery and enforcing value-centric prioritization. AI is fundamentally different from prior technologies, so it requires new capabilities and roles. AI Platform Engineers: AI isn’t a standalone technology, so a multi-technology platform is crucial. Agentic Workflow Engineers: Workflows must be reengineered for AI to deliver value. Bolt-on AI doesn’t deliver enough value to justify the costs. Hardware Optimization Engineers: Keeping training and inference costs low is a massive competitive advantage. It makes more use cases economically feasible and delivers higher margins. AI Ops Engineers: AI in production requires constant attention and modification to ensure reliable operation. AI Evaluation & Quality Engineers: Reliability is another massive competitive advantage. AI must work within specific guarantees, or customers won’t pay for it, and internal users won’t adopt it. What roles am I missing (I left one out on purpose)? What is your business doing to transition its legacy technical teams into value-centric AI teams?

I recently went through the Prompt Engineering guide by Lee Boonstra from Google, and it offers valuable, practical insights. It confirms that getting the best results from LLMs is an iterative engineering process, not just casual conversation. Here are some key takeaways I found particularly impactful: 1. 𝐈𝐭'𝐬 𝐌𝐨𝐫𝐞 𝐓𝐡𝐚𝐧 𝐉𝐮𝐬𝐭 𝐖𝐨𝐫𝐝𝐬: Effective prompting goes beyond the text input. Configuring model parameters like Temperature (for creativity vs. determinism), Top-K/Top-P (for sampling control), and Output Length is crucial for tailoring the response to your specific needs. 2. 𝐆𝐮𝐢𝐝𝐚𝐧𝐜𝐞 𝐓𝐡𝐫𝐨𝐮𝐠𝐡 𝐄𝐱𝐚𝐦𝐩𝐥𝐞𝐬: Zero-shot, One-shot, and Few-shot prompting aren't just academic terms. Providing clear examples within your prompt is one of the most powerful ways to guide the LLM on desired output format, style, and structure, especially for tasks like classification or structured data generation (e.g., JSON). 3. 𝐔𝐧𝐥𝐨𝐜𝐤𝐢𝐧𝐠 𝐑𝐞𝐚𝐬𝐨𝐧𝐢𝐧𝐠: Techniques like Chain of Thought (CoT) prompting – asking the model to 'think step-by-step' – significantly improve performance on complex tasks requiring reasoning (logic, math). Similarly, Step-back prompting (considering general principles first) enhances robustness. 4. 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 𝐚𝐧𝐝 𝐑𝐨𝐥𝐞𝐬 𝐌𝐚𝐭𝐭𝐞𝐫: Explicitly defining the System's overall purpose, providing relevant Context, or assigning a specific Role (e.g., "Act as a senior software architect reviewing this code") dramatically shapes the relevance and tone of the output. 5. 𝐏𝐨𝐰𝐞𝐫𝐟𝐮𝐥 𝐟𝐨𝐫 𝐂𝐨𝐝𝐞: The guide highlights practical applications for developers, including generating code snippets, explaining complex codebases, translating between languages, and even debugging/reviewing code – potential productivity boosters. 6. 𝐁𝐞𝐬𝐭 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞𝐬 𝐚𝐫𝐞 𝐊𝐞𝐲: Specificity: Clearly define the desired output. Ambiguity leads to generic results. Instructions > Constraints: Focus on telling the model what to do rather than just what not to do. Iteration & Documentation: This is critical. Documenting prompt versions, configurations, and outcomes (using a structured template, like the one suggested) is essential for learning, debugging, and reproducing results. Understanding these techniques allows us to move beyond basic interactions and truly leverage the power of LLMs. What are your go-to prompt engineering techniques or best practices? Let's discuss! #PromptEngineering #AI #LLM

In recent months, I had popular episodes on how A.I. is automating and disrupting the advertising and journalism industries. Today, I'm giving the legal profession the treatment. A.I. TOOLS IN LAW TODAY • Contract review, legal research and document automation are now A.I.-powered, saving lawyers hundreds of hours on those tasks annually. • A.I. can scan documents, flag risks, and draft memos in seconds rather than hours. • Litigation prediction tools analyze past case data to forecast potential outcomes. • The result: more accurate work, faster client service and dramatically reduced administrative burden. IMPACT ON LEGAL CAREERS • For lawyers embracing these tools, A.I. becomes a competitive superpower rather than a threat. • Law schools are updating curricula to prepare the next generation for an A.I.-integrated profession. • Brand-new opportunities emerging in compliance, data privacy and A.I. regulation (areas requiring human judgment and ethical reasoning)... entry-level associates adapting to these specialized needs may find themselves in greater demand than traditional roles. • Paralegals are transitioning from document review to A.I.-system supervision, output validation and data workflow management. • Legal technologists and hybrid law-tech roles are emerging as high-demand career paths. • The key is evolution: People aren't being left behind if they adapt with the changing landscape. NOTABLE PLATFORMS • Harvey (I learned at a bar last night this name comes from the TV show "Suits"): OpenAI-backed natural-language co-pilot deployed across major firms for contract drafting and case-law summarization. • CoCounsel: Handles research, deposition prep, and contract analysis... Impressive enough that Thomson Reuters acquired the company for $650 million. BOTTOM LINE • Rather than making attorneys and other legal professionals obsolete, A.I. allows focus on "human" skills like persuasion, critical judgment and empathy. Listen to today's episode of my podcast (Episode #926) to hear more on all of the above! The "Super Data Science Podcast with Jon Krohn" is available on all major podcasting platforms and YouTube. See below for quick access ⬇️ #superdatascience #ai #automation #law #LegalAI #lawyer #paralegal

The best legal leaders don't block innovation. They help build it. They're not just legal advisors. They're business architects who open gates to new markets, shape innovation, and design outcomes that align with business goals and legal obligations. When general counsels are brought into the product lifecycle early, they give business teams clarity on regulatory requirements and legal frameworks across different jurisdictions. Core privacy and legal questions get answered upfront, and vendor contracts get a proper review to ensure they address AI use and include the right privacy and security provisions.   This helps companies improve products and makes them more defensible. It could be the difference between catching risks before a new AI feature or product ships and never having these risks to begin with. Ready to learn more? Tune into this week's She Said Privacy/He Said Security podcast where Justin Daniels and I chat with Smrithi Mohan, General Counsel at Awesome(SmugMug and Flickr), about: 🔷 Smrithi Mohan's career journey building legal, privacy, IP, and innovation functions at global companies 🔷 How the general counsel role is evolving from legal gatekeeper to proactive business architect 🔷 The importance of embedding legal teams early in product and AI development 🔷 Tips for building relationships across teams to drive collaboration 🔷 Core privacy and legal questions to ask during AI development 🔷 Strategies for addressing AI use and risks in legacy vendor contracts 🔷 Legal gray areas in AI-generated outputs and derivative works 🔷 Smrithi's personal privacy tip 🎧 Listen to the full podcast here: https://lnkd.in/eVi-FrFQ   ♻️ Share this episode with your legal, privacy, and operational teams. 🛎️ Subscribe to the She Said Privacy/He Said Security podcast.