senior AI/ML engineer

Hiringhood; Chennai; On-site; INR 1,500,000 - 2,100,000; Full time

Job summary

Hiringhood in Chennai invites experienced AI/ML engineers to design, develop and deploy Generative AI solutions from concept to production. You will build LLM-based applications, RAG, agentic workflows and scalable APIs for enterprise platforms.

You should have 4–8+ years of AI/ML experience, strong Python and NLP skills, and hands-on experience with FastAPI/Flask, REST/microservices, SQL and vector search technologies. Join a product-focused team driving measurable outcomes.

Qualifications

• Healthcare domain experience is a must.
• 4–8+ years in AI/ML, Applied ML, Data Science or Software Engineering.
• Proficient in Python and NLP.
• Experience with FastAPI or Flask.
• Hands-on with REST APIs and microservices.
• Strong SQL and data processing (structured/unstructured).
• Production‑quality coding and production‑grade GenAI apps.
• Experience with RAG, embeddings and vector search.
• AI evaluation, guardrails and governance practices.

Skills

Python

NLP

Production-grade coding

Tools

FastAPI

Flask

REST APIs

Microservices

SQL

LangChain

Pinecone

Qdrant

Weaviate

Milvus

Job description

• 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-
• 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.

Must have:

• Healthcare domain experience is must
• Experience: 4–8+ years in AI/ML | Applied ML | Data Science | Software Engineering
• Python
• NLP
• FastAPI | Flask
• REST APIs | Microservices
• SQL
• Structured | Unstructured Data Processing
• Production-quality coding
• Production-grade LLM / GenAI Applications
• RAG | Embeddings | Vector Search
• Semantic Search | Hybrid Search
• Reranking
• Agentic AI
• Human-in-the-loop
• Multi-agent Workflows
• AI Evaluation
• AI Monitoring
• AI Security
• Scalability
• Cost Optimization
• LangChain | LangGraph | LlamaIndex | Semantic Kernel | AutoGen | CrewAI | Equivalent
• Pinecone | Qdrant | Weaviate | Milvus | FAISS | pgvector | Elasticsearch | OpenSearch

Good to have:

• LoRA | QLoRA | PEFT
• Small Language Models (SLMs)
• MLOps | LLMOps
• Model/Prompt Versioning
• AI Evaluation Frameworks
• Accuracy | Groundedness | Hallucination | Retrieval Relevance | Latency | Safety | Cost Evaluation
• AI Guardrails
• Auditability
• Inference Cost Optimization
• Response Time Optimization
• Infrastructure Optimization
• Multiple AI/ML/GenAI Projects
• Multiple Business Domains
• Product-based Company / Product Organization
• Enterprise AI Integrations
• Databases | APIs | Third-party Applications
• Customer Discussions
• Technical Demonstrations