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Senior AI/ML Engineer Generative AI, LLMs & Agentic AI Experience: 6-8+ years Location: Chennai/Coimbatore Employment Type: Full time Industry: Healthcare Technology / Artificial Intelligence About the Role We are looking for a hands-on Senior AI/ML Engineer to design, build and launch production-grade AI solutions for healthcare and enterprise use cases. The ideal candidate will have strong experience in Machine Learning, Generative AI, Large Language Models, Small Language Models, Retrieval-Augmented Generation and Agentic AI systems. You should be able to take a business problem from discovery and solution design through development, deployment, launch and continuous improvement. This is not a research-only or proof-of-concept role. You will work closely with Product Managers, Business Analysts, healthcare SMEs, architects and engineering teams to build AI products that are scalable, secure, measurable and commercially valuable. Key Responsibilities - Design, develop and deploy AI/ML and Generative AI solutions from concept to production. - Build applications using LLMs, SLMs, RAG, GraphRAG, AI agents, NLP and machine-learning models. - Develop agentic workflows involving tool calling, planning, memory, routing, human-in-the-loop controls and multi-agent orchestration. - Build and optimize retrieval pipelines using embeddings, semantic search, hybrid search, vector databases, reranking and metadata filtering. - Integrate AI solutions with enterprise platforms, databases, APIs, healthcare systems and third-party applications. - Evaluate and select the right approach across traditional ML, LLMs, SLMs, prompt engineering, fine-tuning, RAG and deterministic workflows. - Build evaluation frameworks to measure accuracy, groundedness, hallucination, retrieval relevance, latency, safety and cost. - Implement AI guardrails, structured outputs, confidence scoring, fallback mechanisms and auditability. - Create scalable APIs and microservices for AI-powered products. - Implement MLOps and LLMOps practices, including model and prompt versioning, monitoring, tracing, CI/CD and production observability. - Optimize model performance, token consumption, inference cost, response time and infrastructure usage. - Translate business requirements into technical solutions, architecture, delivery plans and measurable outcomes. - Participate in product discovery, customer discussions, technical demonstrations and solution reviews. - Mentor junior engineers and contribute to AI engineering standards and reusable frameworks. Required Skills and Experience - 48+ years of experience in AI/ML, data science, applied machine learning or software engineering. - Strong programming experience in Python with production-quality coding practices. - Hands-on experience building and deploying applications using LLMs or Generative AI models. - Practical experience with RAG pipelines, AI agents, prompt engineering, embeddings and vector search. - Strong understanding of machine learning, deep learning, NLP, model evaluation and data processing. - Experience with AI orchestration frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, AutoGen, CrewAI or equivalent. - Experience with model platforms such as OpenAI, Azure OpenAI, Anthropic Claude, AWS Bedrock, Google Vertex AI, Hugging Face, Llama or Mistral. - Experience with vector databases or search platforms such as Pinecone, Weaviate, Qdrant, Milvus, FAISS, pgvector, Elasticsearch or OpenSearch. - Experience developing REST APIs and microservices using FastAPI, Flask or similar frameworks. - Experience with SQL, relational databases and structured and unstructured data processing. - Experience deploying applications on AWS, Microsoft Azure or Google Cloud Platform. - Working knowledge of Docker, Git, automated testing and CI/CD. - Understanding of AI security, privacy, scalability, monitoring, latency and cost optimization. - Proven experience taking at least one AI/ML product or solution from inception to production launch. - Strong problem-solving, critical-thinking and stakeholder communication skills. Preferred Skills - Experience in healthcare, life sciences, pharmaceuticals, payer, provider or patient-support solutions. - Understanding of healthcare workflows such as patient engagement, prior authorization, benefits verification, clinical documentation, claims or pharmacovigilance. - Familiarity with FHIR, HL7, EHR/EMR integrations, HIPAA and PHI data-handling requirements. - Experience with Small Language Models, model fine-tuning, quantization or open-source model deployment. - Experience with NVIDIA AI technologies, GPU inference or model optimization. - Experience with multimodal AI involving documents, voice, audio, images or conversational interfaces. - Experience working in a startup, product organization or customer-facing solution engineering environment. - Knowledge of responsible AI, model governance, bias management and explainability. What
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.
