Primary Responsibilities :

- Design, develop, and deploy production-ready AI/ML solutions for enterprise applications.

- Build and optimize Large Language Model (LLM) applications, AI agents, and Retrieval-Augmented Generation (RAG) pipelines.

- Fine-tune, evaluate, and optimize open-source and commercial AI models for specific business use cases.

- Develop scalable APIs and AI services using Python frameworks such as FastAPI or Flask.

- Build intelligent automation workflows using AI, vector databases, and orchestration frameworks.

- Design and implement machine learning pipelines for data preprocessing, feature engineering, model training, validation, and deployment.

- Collaborate with Product Managers, Architects, UI/UX Designers, and DevOps teams to deliver AI-powered products.

- Optimize model inference performance, latency, and infrastructure costs.

- Mentor junior AI engineers and participate in architecture discussions, technical reviews, and code reviews.

- Research and evaluate emerging AI technologies and recommend adoption where appropriate.

- Ensure AI applications follow security, privacy, and responsible AI best practices.

Technical Requirements :

1. Programming Languages :

- Strong expertise in Python.

- Good knowledge of SQL.

- Basic understanding of JavaScript or TypeScript for AI integrations.

2. Artificial Intelligence & Machine Learning :

- Strong experience with Machine Learning, Deep Learning, NLP, and Generative AI.

- Hands-on experience with LLMs such as GPT, Claude, Llama, Qwen, Mistral, or Gemma.

- Experience developing AI Agents and multi-agent workflows.

- Strong understanding of prompt engineering, structured outputs, function calling, and tool integration.

- Experience implementing Retrieval-Augmented Generation (RAG) architectures.

- Knowledge of embedding models and semantic search.

3. AI Frameworks :

- LangChain

- LangGraph

- LlamaIndex

- Haystack (preferred)

- OpenAI SDK, Hugging Face Transformers, Sentence Transformers.

4. Machine Learning Frameworks :

- PyTorch

- TensorFlow

- Scikit-learn

- XGBoost, LightGBM.

5. Vector Databases :

- Pinecone

- Qdrant

- Milvus

- ChromaDB, FAISS.

6. Backend Development :

- FastAPI

- Flask

- RESTful APIs

- WebSockets

- Background workers (Celery/RQ).

7. Databases :

- PostgreSQL, MongoDB, Redis.

8. Cloud & Infrastructure :

- AWS, Azure, or Google Cloud Platform

- Docker, Kubernetes (preferred)

- Git, CI/CD pipelines, Linux environments.

9. MLOps :

- MLflow, Weights & Biases

- Model versioning

- Model deployment

- Monitoring and observability

- GPU optimization and inference serving.

10. Computer Vision :

- OpenCV, YOLO, OCR.

General Requirements :

- Strong knowledge of software architecture and design patterns.

- Experience building scalable AI SaaS platforms.

- Understanding of API security, authentication, and data privacy.

- Excellent debugging and performance optimization skills.

- Familiarity with Agile/Scrum development methodology.

Experience :

- Minimum Years of Experience : 4+ Years.

- Relevant Industry Experience : 4+ years in Artificial Intelligence, Machine Learning, and production software development with at least 2 years of hands-on experience in Generative AI and LLM-based applications.

Leadership & Communication :

- Ability to independently own AI features from design through production deployment.

- Mentor junior AI engineers and share technical knowledge across the team.

- Excellent analytical and problem-solving skills.

- Strong communication skills with the ability to explain complex AI concepts to technical and non-technical stakeholders.

- Experience participating in client meetings, requirement discussions, and technical solution presentations.

Require Client Communication :

- Yes