About the Role :

We are seeking a highly skilled Lead AI Engineer to design, build, and scale enterprise-grade Agentic AI and Generative AI solutions. This role combines AI engineering, software architecture, cloud-native development, and business transformation expertise.

You will lead the development of autonomous AI agents, multi-agent systems, AI copilots, Retrieval-Augmented Generation (RAG) platforms, and AI-powered business applications that drive measurable business outcomes.

The ideal candidate possesses deep expertise in Large Language Models (LLMs), agent orchestration frameworks, AI platform engineering, cloud infrastructure, and enterprise software development.

Key Responsibilities :

Agentic AI & GenAI Engineering :

- Design and develop enterprise-grade AI agents and multi-agent systems.

- Build autonomous workflows involving planning, reasoning, memory, tool usage, and orchestration.

- Develop AI copilots, virtual assistants, and intelligent automation solutions.

- Implement agent-to-agent (A2A) communication patterns and agent memory architectures.

- Create reusable frameworks and accelerators for Agentic AI adoption.

Generative AI Solutions :

- Develop LLM-powered applications using proprietary and open-source models.

- Design and optimize prompt engineering strategies.

- Fine-tune foundation models using LoRA, PEFT, and supervised fine-tuning techniques.

- Evaluate model performance, hallucination rates, latency, and cost efficiency.

- Implement multimodal AI solutions involving text, image, speech, and structured data.

Retrieval-Augmented Generation (RAG) :

- Design scalable RAG architectures.

- Build document ingestion, embedding, indexing, and retrieval pipelines.

- Implement semantic, vector, hybrid, and GraphRAG search solutions.

- Optimize retrieval quality and response grounding.

AI Platform Engineering :

- Architect AI platforms supporting model lifecycle management.

- Build AI observability, evaluation, monitoring, and governance capabilities.

- Develop internal AI platforms enabling secure enterprise AI adoption.

- Enable model deployment, versioning, experimentation, and rollback mechanisms.

Enterprise Integration :

- Integrate AI solutions with enterprise applications, APIs, ERP, CRM, collaboration platforms, and business workflows.

- Develop MCP-based integrations and AI tool ecosystems.

- Connect AI agents with databases, knowledge repositories, SaaS platforms, and business systems.

Cloud & MLOps :

- Deploy AI workloads on AWS, Azure, and GCP.

- Build scalable AI infrastructure using Kubernetes and containerized architectures.

- Implement CI/CD pipelines for AI applications.

- Establish LLMOps, MLOps, monitoring, security, and governance frameworks.

Leadership & Consulting :

- Collaborate with business stakeholders to identify AI use cases and ROI opportunities.

- Translate business challenges into scalable AI solutions.

- Lead architecture reviews, technical decisions, and AI strategy initiatives.

- Mentor AI engineers, data scientists, and software developers.

- Drive AI adoption and organizational transformation.

Required Technical Skills :

AI & Machine Learning :

- Large Language Models (GPT, Claude, Gemini, Llama, Mistral)

- Prompt Engineering

- Fine-Tuning Techniques

- Reinforcement Learning Concepts

- Agentic AI Systems

- Multi-Agent Architectures

- RAG & GraphRAG

- Embeddings & Semantic Search

- Model Evaluation Frameworks

- Multimodal AI

Agent Frameworks :

- LangGraph

- CrewAI

- AutoGen

- Google ADK

- OpenAI Agents SDK

- Semantic Kernel

- MCP (Model Context Protocol)

Programming :

- Python (Expert)

- SQL

- Java or Go (Preferred)

- REST APIs

- GraphQL

AI Frameworks :

- LangChain

- Hugging Face

- PyTorch

- TensorFlow

- OpenAI APIs

- Azure OpenAI

- Vertex AI

- Bedrock

Vector Databases :

- Pinecone

- Weaviate

- FAISS

- Chroma

- Milvus

- Azure AI Search

Cloud Platforms :

- AWS

- Microsoft Azure

- Google Cloud Platform

Infrastructure :

- Docker

- Kubernetes

- Terraform

- GitHub Actions

- Jenkins

- ArgoCD

Data Technologies :

- Databricks

- Spark

- Fabric

- BigQuery

- Snowflake

Preferred Qualifications :

- Bachelor's or Master's degree in Computer Science, AI, Data Science, Engineering, or related field.

- AI/ML certifications from Azure, AWS, Google Cloud, NVIDIA, or equivalent.

- Contributions to open-source AI projects.

- Experience building enterprise-scale AI platforms.

- Experience deploying production-grade AI systems serving thousands of users.