Artificial Intelligence Engineer

Position : - AI Engineer

4–6 years of hands-on expertise in AI / ML, Generative AI, and Agentic AI systems for Pune location.

Key Responsibilities

Design, build, and deploy AI / ML models for classification, prediction, recommendation, and optimization.

Build and deploy REST APIs for ML / AI models using frameworks such as Flask or FastAPI .

Apply asynchronous programming and high-performance API design .

Work with Generative AI frameworks (LLMs, Diffusion models, Transformers, etc.) to develop next-gen AI solutions.

Build and orchestrate Agentic AI systems capable of reasoning, planning, and executing tasks autonomously.

Fine-tune and optimize Large Language Models (LLMs) for domain-specific use cases.

Manage model versioning, monitoring, and scaling for APIs in production environments.

Implement API authentication and security (JWT, OAuth, etc.).

Integrate AI models with APIs, databases, and enterprise systems for end-to-end workflows.

Evaluate model performance, scalability, and reliability , ensuring alignment with business goals.

Research, prototype, and productionize cutting-edge AI techniques .

Collaborate with product managers, data scientists, and engineers to deliver scalable AI-driven applications.

Required Skills & Experience

4–6 years of hands-on experience in AI / ML engineering .

Strong coding skills in Python (NumPy, Pandas, Scikit-learn, PyTorch, TensorFlow).

Expertise in Generative AI (LLMs like GPT, LLaMA, Claude, diffusion models, etc.).

Experience with Agentic AI frameworks (LangChain, LlamaIndex, AutoGPT, CrewAI, or similar).

Solid understanding of NLP, embeddings, and vector databases (Pinecone, Weaviate, FAISS, Milvus).

Strong knowledge of the ML lifecycle (data preprocessing, training, evaluation, deployment).

Expertise in building REST APIs with FastAPI / Flask and integrating them into applications.

Experience with containerized solutions using Docker & Kubernetes .

Deployment experience across APIs, containers, and cloud platforms (AWS / GCP / Azure).

Proficiency in prompt engineering, RAG (Retrieval Augmented Generation) , and model fine-tuning .

Strong problem-solving, debugging, and optimization skills .