Role : AI Lead Engineer

Role & Responsibilities :

The AI Lead Engineer will be responsible for architecting, developing, and delivering enterprise-scale Artificial Intelligence (AI), Machine Learning (ML), and Generative AI (GenAI) solutions. The role involves leading engineering teams, defining AI architecture, building production-ready LLM applications, implementing MLOps best practices, and driving end-to-end AI solution delivery across cloud environments.

Technical Skills & Expertise :

Artificial Intelligence & Machine Learning :

- Machine Learning

- Deep Learning

- Generative AI (GenAI)

- Large Language Models (LLMs)

- Natural Language Processing (NLP)

- Computer Vision (Good to Have)

- Reinforcement Learning (Preferred)

LLMs & AI Frameworks :

- OpenAI

- Azure OpenAI

- Claude

- Gemini

- Llama

- Mistral

- Hugging Face Transformers

- LangChain

- LangGraph

- LlamaIndex

- Agentic AI Frameworks

- Retrieval-Augmented Generation (RAG)

- Prompt Engineering

Programming :

- Python

- SQL

- REST APIs

- FastAPI

- Object-Oriented Programming (OOP)

Machine Learning Frameworks :

- TensorFlow

- PyTorch

- Scikit-learn

- XGBoost

- Pandas

- NumPy

MLOps & LLMOps :

- MLflow

- Kubeflow

- Airflow

- Model Deployment

- Model Monitoring

- CI/CD for ML

- Model Versioning

- Experiment Tracking

- LLMOps Best Practices

Vector Databases :

- Pinecone

- FAISS

- ChromaDB

- Weaviate

- Qdrant

Cloud Platforms :

- Microsoft Azure

- Amazon Web Services (AWS)

- Google Cloud Platform (GCP)

- Azure AI Services

- Vertex AI

- Amazon SageMaker

DevOps & Containerization :

- Docker

- Kubernetes

- Git

- CI/CD Pipelines

- Terraform (Preferred)

Architecture & Governance :

- Enterprise AI Architecture

- AI Solution Design

- Responsible AI

- AI Security

- Scalability & Performance Optimization

- AI Governance

Preferred Candidate Profile :

1. AI Solution Architecture :

- Architect enterprise-scale AI, ML, and Generative AI solutions aligned with business objectives.

- Design scalable AI platforms, LLM-powered applications, and intelligent automation solutions.

- Define enterprise AI architecture standards, reusable frameworks, and engineering best practices.

- Evaluate trade-offs across scalability, performance, security, and cost.

2. Generative AI & LLM Development :

- Lead the design and implementation of LLM-powered applications using OpenAI, Azure OpenAI, Claude, Gemini, Llama, and other foundation models.

- Build Retrieval-Augmented Generation (RAG) pipelines, AI agents, and conversational AI systems.

- Develop prompt engineering strategies and AI workflow orchestration.

- Design and optimize vector database architectures and embedding pipelines.

3. Machine Learning & MLOps :

- Lead the end-to-end machine learning lifecycle, including model development, deployment, monitoring, and optimization.

- Implement MLOps and LLMOps best practices using MLflow, Kubeflow, Airflow, and cloud-native AI services.

- Ensure model reliability, scalability, and continuous improvement through automated pipelines.

- Drive experimentation, model versioning, and performance monitoring.

4. Cloud & Platform Engineering :

- Design and deploy AI solutions across Azure, AWS, and GCP.

- Build cloud-native AI platforms using containerized deployments with Docker and Kubernetes.

- Optimize AI infrastructure for performance, resilience, and operational efficiency.

- Implement CI/CD pipelines and Infrastructure as Code for AI workloads.

5. Leadership & Team Management :

- Lead and mentor AI/ML engineers, data scientists, and software engineers.

- Drive technical decision-making, architecture reviews, and code quality.

- Collaborate with Product Managers, Architects, Data Engineers, and business stakeholders to deliver AI initiatives.

- Foster a culture of innovation, continuous learning, and engineering excellence.

6. Innovation & Research :

- Stay current with advancements in AI, LLMs, deep learning, and emerging technologies.

- Evaluate and implement new AI frameworks, tools, and architectures.

- Drive proof-of-concepts (POCs), innovation initiatives, and enterprise AI adoption.

Mandatory Skills :

- 9-11 years of experience in AI/ML engineering with enterprise application development.

- Strong expertise in Python and modern AI/ML frameworks.

- Hands-on experience with Generative AI, Large Language Models (LLMs), and Retrieval-Augmented Generation (RAG).

- Experience with LangChain, LangGraph, LlamaIndex, and AI orchestration frameworks.

- Strong knowledge of MLOps/LLMOps, model deployment, monitoring, and automation.

- Experience with cloud platforms (Azure, AWS, or GCP) and AI services.

- Expertise in Docker, Kubernetes, and CI/CD pipelines.

- Strong leadership, architecture, stakeholder management, and mentoring skills.

Nice to Have :

- Experience with Multi-Agent AI Systems and Agentic AI.

- Knowledge of Model Context Protocol (MCP) and AI Guardrails.

- Experience with GraphRAG, Knowledge Graphs, or Semantic Search.

- Exposure to NVIDIA NIM, vLLM, Ollama, or model serving frameworks.

- Azure AI Engineer Associate, Google Professional Machine Learning Engineer, or AWS Machine Learning Specialty certification.

- Experience delivering enterprise AI solutions in regulated or large-scale environments.