Lead Artificial Intelligence Engineer

Description :

Open Position : Lead Artificial Intelligence Engineer

Location : Baner, Pune (Hybrid)

Experience : 6+ Years

Shift : 2 : 00 PM 11 : 00 PM IST

Notice Period : Immediate 15 Days

Core Expertise Required :

Python | Generative AI | LLMs | AI Agents | NLP | PyTorch | TensorFlow | MLOps | System Architecture | CI/CD

Role Overview :

We are looking for a Lead Artificial Intelligence Engineer with strong expertise in Generative AI, NLP, AI Agents, and MLOps, who can architect, lead, and deliver scalable AI solutions for real-world business problems.

This role requires a hands-on technical leader who can design end-to-end AI systems, mentor a team of AI engineers and data scientists, and drive the development of production-grade AI platforms including LLM-powered applications, AI agents, and intelligent automation systems.

The ideal candidate will combine deep AI/ML expertise, leadership capabilities, and system architecture skills to lead AI initiatives from research and prototyping to deployment and scaling.

Key Responsibilities :

AI Leadership & Team Management :

- Lead and mentor a team of AI Engineers, ML Engineers, and Data Scientists.

- Drive technical decision-making and architectural design for AI systems.

- Establish best practices for AI development, experimentation, and deployment.

- Guide the team in model selection, evaluation strategies, and production readiness.

- Collaborate with product, engineering, and business stakeholders to align AI initiatives with organizational goals.

Generative AI & AI Agent Development :

- Design and develop Generative AI applications using LLMs.

- Build and deploy AI Agents and autonomous workflows using modern frameworks.

- Implement RAG (Retrieval Augmented Generation) pipelines.

- Optimize prompt engineering, fine-tuning, embeddings, and vector search pipelines.

- Work with vector databases such as Pinecone, Weaviate, FAISS, or similar platforms.

NLP & AI Model Development :

- Design and implement advanced NLP solutions for tasks such as classification, summarization, semantic search, and conversational AI.

- Build and optimize ML and deep learning models using PyTorch, TensorFlow, and Scikit-Learn.

- Develop scalable inference services and APIs using FastAPI, Flask, or Django.

- Work with transformer models, embeddings, and modern NLP architectures.

AI Evaluation & Model Quality :

- Define and implement robust AI evaluation frameworks.

- Apply advanced metrics such as BLEU, ROUGE, perplexity, ranking metrics, and human evaluation strategies.

- Measure model reliability, bias, hallucination risks, and response quality in LLM systems.

- Design A/B testing frameworks and validation pipelines for production models.

MLOps & AI Platform Engineering :

- Design and manage end-to-end ML pipelines.

- Implement CI/CD pipelines for AI workflows using Git, Docker, and automation tools.

- Deploy and manage models on AWS, GCP, or Azure.

- Implement model monitoring, drift detection, retraining strategies, and experiment tracking.

- Build scalable AI infrastructure supporting training, inference, and monitoring.

System Architecture & Scalability :

- Architect enterprise-grade AI systems integrating data pipelines, model training, inference, and monitoring.

- Design microservices-based AI platforms.

- Ensure high scalability, performance, and reliability of AI applications.

Required Qualifications :

- Bachelors or Masters degree in Computer Science, Artificial Intelligence, Data Science, or related fields.

- 6+ years of hands-on experience in AI/ML development and deployment.

- Strong expertise in Python and AI/ML frameworks (PyTorch, TensorFlow, Scikit-Learn).

- Deep experience with Generative AI, LLMs, NLP, and embeddings.

- Hands-on experience with AI evaluation frameworks and model quality assessment.

- Strong understanding of MLOps practices, CI/CD pipelines, and containerization (Docker).

- Experience building production-ready AI systems at scale.

- Proven experience leading or mentoring AI/ML teams.

Preferred Qualifications :

- Experience with AI Agent frameworks and orchestration tools.

- Exposure to LangChain, LlamaIndex, or similar GenAI ecosystems.

- Experience with Big Data technologies such as Spark, Kafka, or Kinesis.

- Familiarity with Vector Databases and semantic search systems.

- Experience with ML experiment tracking tools (MLflow, Weights & Biases).

- Knowledge of cloud AI platforms such as AWS SageMaker or GCP Vertex AI.