Sr. Development Lead

Job Description

Sr. Development Lead

Noida, Uttar Pradesh

Job Summary

Role Title:

Generative AI Engineer / Applied AI Developer

Experience Required:

Minimum 5+ Years in Software / Machine Learning (with 1.5+ years hands-on in LLMs & GenAI)

Core Tech Stack:

Python, PyTorch, LangChain, LlamaIndex, Vector DBs, FastAPI, Docker, Cloud AI (AWS/Azure)

Target Work Model:

Hybrid (Enterprise Office / Remote flexibility available)

Employment Type:

Full-Time, Permanent Position

We are seeking a talented, technically passionate Generative AI Engineer with 3+ years of professional software engineering and applied AI/ML experience to design, build, and deploy production-grade LLM applications. In this hands-on engineering role, you will turn the latest advancements in generative artificial intelligence into resilient, scalable business solutions.

You will specialize in constructing Advanced Retrieval-Augmented Generation (RAG) pipelines, engineering autonomous agents with deterministic tool-calling capabilities, optimizing prompt workflows, experimenting with parameter-efficient model adaptation (LoRA/QLoRA), and serving high-performance inference APIs via containerized microservices hosted on AWS or Microsoft Azure.

Key Responsibilities

RAG Architecture & Vector Search Engineering

• RAG Pipeline Development: Design and implement end-to-end RAG pipelines incorporating multi-modal document parsing, recursive text splitting, and semantic chunking.
• Vector Store Management: Integrate, optimize, and benchmark vector databases (Pinecone, Qdrant, Chroma, Weaviate, pgvector, Azure AI Search) for sub-second similarity search.
• Hybrid Search & Re-ranking: Implement hybrid retrieval combining dense vector embeddings with sparse lexical keyword matching (BM25) and cross-encoder re-ranking models (Cohere Rerank, BGE).
• Context Optimization: Engineer contextual compression, parent-document retrieval, and metadata filtering schemas to eliminate context noise and hallucinations.

B. Agentic Workflows & Multi-Tool Integration

• Agentic Frameworks: Build stateful multi-agent workflows and autonomous task execution loops using LangChain, LangGraph, LlamaIndex, or AutoGen.
• Tool Calling & Action APIs: Develop robust, type-safe function-calling and tool-use interfaces connecting LLMs to external REST APIs, relational databases, and enterprise systems.
• Loop Safeguards & Routing: Implement plan-and-solve agent patterns, multi-step reflection loops, and fallback error-handling to prevent infinite agent recursion.

C. Prompt Engineering, Output Validation & Fine-Tuning

• Prompt Engineering: Design and test systematic prompt templates utilizing Chain-of-Thought (CoT), Few-Shot prompting, ReAct patterns, and directional stimulus prompting.
• Structured Output Parsing: Enforce deterministic, validated output structures using Pydantic, Instructor, Guidance, or Outlines to guarantee reliable downstream ingestion.
• Model Adaptation: Conduct Parameter-Efficient Fine-Tuning (PEFT, LoRA, QLoRA) on open-weights models (Llama 3, Mistral, Phi-3/4) for domain-specific tasks.

D. Backend API & Inference Microservices

• Inference APIs (FastAPI): Develop high-throughput asynchronous backend microservices using Python (FastAPI), managing connection pooling, concurrency, and rate limiting.
• Streaming Latency: Implement low-latency streaming inference pipelines utilizing Server-Sent Events (SSE) and WebSockets for conversational user interfaces.
• Inference Engines: Optimize inference throughput utilizing modern serving engines like vLLM, TensorRT-LLM, or Ollama, minimizing Time-to-First-Token (TTFT).
• Containerized Deployment: Containerize applications with Docker and configure deployment manifests for Kubernetes, AWS ECS/EKS, or Azure Container Apps.

E. Evaluation, Guardrails & LLMOps Observability

• Automated Model Evaluation: Establish automated evaluation benchmarks utilizing synthetic test datasets and evaluation harnesses (Ragas, TruLens, DeepEval) to quantify faithfulness and recall.
• AI Guardrails & Safety: Implement enterprise guardrails (NeMo Guardrails, Llama Guard, Azure Content Safety) to proactively mitigate prompt injection, jailbreaks, and toxicity.
• Observability & Tracing: Integrate comprehensive LLM tracing, prompt versioning, and cost monitoring using tools like Langfuse, Phoenix, or Weights & Biases.

Skill Requirements

Required Technical Skills & Qualifications

• Total Experience: Minimum 3+ years of professional software engineering / data science experience, with at least 1.5+ years of dedicated hands-on GenAI / LLM development.
• Education: Bachelor's degree in Computer Science, Software Engineering, Artificial Intelligence, Data Science, or equivalent technical field.
• Python & ML Foundations: Advanced Python programming skills (asyncio, type hints, Pydantic, pytest) and deep familiarity with PyTorch or Hugging Face Transformers.
• GenAI Frameworks: Extensive hands-on experience with LangChain, LangGraph, LlamaIndex, and commercial/open foundation model APIs (OpenAI, Anthropic, Bedrock).
• Vector Search & RAG: Proven track record building and deploying production RAG architectures using vector databases (Pinecone, Chroma, Qdrant, pgvector).
• Backend & Containers: Hands-on proficiency developing asynchronous REST APIs using FastAPI, Docker containerization, and Git-based collaborative workflows.
• Core Competencies: Strong analytical problem-solving skills, solid understanding of transformer architecture mechanics, and proactive technical curiosity.

4. Preferred Qualifications & Industry Certifications

• Inference Optimization: Hands-on experience with model quantization (GGUF, AWQ, GPTQ) and high-throughput inference serving engines (vLLM, TGI, TensorRT-LLM).
• Cloud Certifications: Cloud AI certifications: AWS Certified AI Practitioner / ML Specialty, Microsoft Certified: Azure AI Engineer Associate (AI-102), or Azure AI Fundamentals.
• Open Source & Portfolio: Demonstrated contributions to open-source GenAI projects, LangChain/LlamaIndex community plugins, or active technical blog/GitHub repository.
• Agile Collaboration: Experience working in Agile/Scrum sprint environments collaborating with Product Managers, UX Designers, and Frontend Developers.

Other Requirements

. Candidate Evaluation Matrix (For Technical Interview Panels)

Technical interviewers should evaluate candidates across the following five core technical engineering competencies:

Evaluation Area

Competency Focus

Key Technical Signals

RAG Architecture & Retrieval

Chunking strategies, vector search, hybrid retrieval, and re-ranking

Explains concrete trade-offs between chunk sizes; demonstrates practical experience with hybrid search (BM25 + vector) and cross-encoders to improve recall.

Agentic Loops & Tool Calling

Function calling, stateful graphs, and error recovery mechanisms

Can code and debug an agent with deterministic tool use; understands state management in LangGraph; handles schema parsing errors gracefully.

Prompt Engineering & Parsing

Few-shot prompting, structured JSON schema validation (Pydantic)

Demonstrates advanced prompting techniques (ReAct, CoT); knows how to force LLMs to emit valid JSON schemas reliably using Instructor or Outlines.

Backend & Microservice APIs

FastAPI, asynchronous streaming, connection pooling, and Docker

Comfortably writes async Python endpoints streaming tokens via SSE; explains how to containerize an application and manage GPU/CPU resource constraints.

Evaluation & LLMOps Guardrails

Synthetic benchmarking, hallucination metrics, and guardrail layers

Articulates how to evaluate an LLM app using Ragas or TruLens; explains how NeMo Guardrails or regex/semantic filters prevent prompt injections.

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Information at a Glance

Why HCLTech?

At HCLTech, youll supercharge your potential. Youll find your career. And youll find your spark. All at a place that knows that helping its customers stay on top starts by putting its people first.

HCLTech is a global technology company, home to more than 230,000 people across 60 countries, delivering industry-leading capabilities centered around digital, engineering, cloud and AI, powered by a broad portfolio of technology services and products. We work with clients across all major verticals, providing industry solutions for Financial Services, Manufacturing, Life Sciences and Healthcare, Technology and Services, Telecom and Media, Retail and CPG, and Public Services. Consolidated revenues as of 12 months ending September 2026 totaled $14.8 billion.