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REQUIREMENT_ID_1481 • 3-DAY_ACTIVE_POLICY N

Senior AI/ML Engineer

Company
Company Connected Value Health Solutions
Type
Opportunity Type Full-Time Job
Salary
Stipend / Salary Competitive Package (Best in Industry)
Location
Location Coimbatore, India
Posted Date
Posted Date Today
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Generation Ml Senior Artificial Intelligence
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Aptitude Practice Questions & Mock Tests Open Resource ↗
Curated logical, quantitative, and verbal reasoning problems for the initial online screening round.
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Company-Specific Interview Preparation Corner Open Resource ↗
Detailed interview experiences, exam formats, and previous test questions for Connected Value Health Solutions and tech roles.
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Technical Placement Cheat Sheets & Question Bank Open Resource ↗
High-yield coding cheat sheets, core CS fundamentals (OOP, DBMS, OS, Networks), and rapid revision guides.
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Algorithm, DSA & Live Code Debugger Practice Open Resource ↗
Hands-on problem sets to improve coding speed and step-by-step memory debugging.

Experience: 5-8 Yrs. Open to matching qualifications.

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Online Assessment / Technical Interview Rounds / HR Review
Senior AI/ML Engineer Generative AI, LLMs & Agentic AI Experience: 5-8+ years Location: [Chennai/Coimbatore] Employment Type: Full-time 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 • 5-8+ 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 We Are Looking For • A builder who can convert ambiguous problems into working AI products. • A product-minded engineer who understands both technical feasibility and business value. • Someone who can challenge assumptions and recommend when AI shouldor should notbe used. • A strong collaborator who can work effectively with Product, Business, Engineering and healthcare teams. • A practical problem solver who takes ownership from solution design through production launch. • Someone who continuously learns and adapts as AI technologies evolve. Why Join Us? • Build real-world healthcare AI products with measurable business impact. • Work across LLMs, SLMs, RAG, Agentic AI, multimodal AI and intelligent automation. • Own solutions across the complete lifecyclefrom discovery to launch. • Influence AI architecture, product direction and engineering standards. • Work in a fast-moving environment where your ideas and technical decisions directly shape products.

Connected Value Health Solutions — AI & Machine Learning Interview Guide

Previously asked questions, exam syllabus, coding benchmarks & round strategy.

APTITUDE & LOGIC 📖
Aptitude Practice Questions & Online Mock Tests

Curated logical, quantitative, and verbal reasoning problems to sharpen reasoning skills required for the initial screening test.

Open Resource ↗
COMPANY GUIDE 🎯
Connected Value Health Solutions Interview Preparation Corner

Comprehensive guide covering typical interview formats, common questions, and preparation tips for Connected Value Health Solutions and off-campus tech roles.

Open Resource ↗
MOCK TESTS & PAPERS 📝
Comprehensive Interview Prep Resources & Syllabus

Collection of previous year questions, company-specific test patterns, and interview experiences for technical and HR rounds.

Open Resource ↗
CODING PRACTICE 💻
Algorithm and Data Structure Problem Set

Practice problems to improve coding proficiency and algorithm problem-solving speed for technical rounds.

Open Resource ↗
Round 1: Online Assessment (OA)
Aptitude, Quantitative Logic & 2 Coding Problems
Focus on accuracy and speed. Practice arrays, strings, and standard arithmetic puzzles.
Round 2: Technical Interview 1
Core Tech Stack (Generation, Ml, Senior, Artificial Intelligence) & Live Code Tracing
Explain your thought process aloud. Analyze time & space complexities before coding.
Round 3: System Design & Problem Solving
Database Schemas, APIs & Architecture Basics
Clarify edge cases, diagram schemas cleanly, and discuss scalability trade-offs.
Round 4: HR & Cultural Fit Discussion
Connected Value Health Solutions Core Values, Learning Agility & Offer Terms
Demonstrate passion, strong communication, and readiness for full-time collaboration.
What is the bias-variance tradeoff and how do you prevent overfitting? Answer ▼
Model Answer: High bias leads to underfitting (oversimplified model), high variance leads to overfitting (captures noise). Mitigate using L1/L2 Regularization, Dropout, Cross-Validation, and data augmentation.
Explain the difference between Precision, Recall, and F1-Score. Answer ▼
Model Answer: Precision = TP / (TP + FP) (correctness of positive predictions). Recall = TP / (TP + FN) (coverage of actual positives). F1-Score is the harmonic mean of Precision and Recall.
How does Gradient Descent work and what is the role of Learning Rate? Answer ▼
Model Answer: It optimizes loss functions by iteratively moving weights in the direction of negative gradient. A large learning rate may overshoot the minimum; a small rate causes slow convergence.
What is the difference between Supervised, Unsupervised, and Self-Supervised learning? Answer ▼
Model Answer: Supervised uses labeled data (X -> y). Unsupervised finds hidden patterns in unlabeled data (clustering/PCA). Self-supervised generates labels from input data (e.g. masked language modeling in BERT/Transformers).
Why do you want to join Connected Value Health Solutions as a Senior AI/ML Engineer?
Preparation Tip: Highlight Connected Value Health Solutions's market reputation, recent tech innovations, and how your skills in Generation directly solve their team's objectives.
Describe a challenging bug or academic project roadblock and how you resolved it.
Preparation Tip: Use the STAR method: Situation (project context), Task (what needed solving), Action (specific tools/logic applied), Result (quantifiable positive outcome).
How do you handle strict deadlines or sudden scope changes?
Preparation Tip: Explain your prioritization strategy, proactive communication with mentors/peers, and agile mindset.

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