Key Responsibilities :

- Design, develop, and deploy AI-driven applications and intelligent software solutions.

- Build and optimize machine learning and deep learning models for production environments.

- Develop applications leveraging Large Language Models (LLMs), Generative AI, and Natural Language Processing (NLP).

- Fine-tune foundation models using domain-specific datasets and implement Retrieval-Augmented Generation (RAG) pipelines.

- Develop AI services and RESTful APIs for seamless integration with enterprise applications.

- Build scalable backend systems to support AI inference and model serving.

- Design data pipelines for model training, evaluation, and continuous improvement.

- Implement prompt engineering techniques to improve LLM performance and response quality.

- Optimize model accuracy, latency, scalability, and infrastructure utilization.

- Collaborate with software engineers, product managers, and data scientists throughout the product lifecycle.

- Deploy AI models using containerization and orchestration technologies.

- Implement MLOps practices for automated model training, deployment, monitoring, and version control.

- Evaluate emerging AI technologies and recommend suitable solutions for business use cases.

- Ensure AI applications meet security, governance, and responsible AI standards.

- Troubleshoot production issues and continuously improve system performance.

Required Skills :

- Strong programming skills in Python.

- Experience with machine learning frameworks such as PyTorch, TensorFlow, or Scikit-learn.

- Hands-on experience with Large Language Models (LLMs) and Generative AI technologies.

- Strong understanding of NLP, Transformers, Embeddings, and Vector Databases.

- Experience building RAG-based applications.

- Knowledge of prompt engineering and LLM optimization techniques.

- Experience with LangChain, LangGraph, LlamaIndex, or similar AI orchestration frameworks.

- Experience developing REST APIs using FastAPI or Flask.

- Strong understanding of data structures, algorithms, and software engineering principles.

- Experience with SQL and NoSQL databases.

- Familiarity with Docker, Kubernetes, and containerized deployments.

- Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform.

- Knowledge of CI/CD pipelines and MLOps tools.

- Familiarity with Git and Agile development methodologies.

Preferred Qualifications :

- Experience working with OpenAI, Anthropic, Google Gemini, or open-source LLMs such as Llama, Mistral, or Qwen.

- Exposure to AI agents, multi-agent systems, and autonomous workflows.

- Experience with vector databases such as Pinecone, Weaviate, Milvus, or ChromaDB.

- Knowledge of distributed computing, GPU optimization, and model serving frameworks.

- Experience with monitoring and observability tools for AI applications.

- Understanding of responsible AI, model governance, and AI security best practices.

Soft Skills :

- Strong analytical and problem-solving abilities.

- Excellent communication and collaboration skills.

- Ability to work independently and in cross-functional teams.

- Passion for innovation and continuous learning in AI technologies.

- Strong ownership, accountability, and attention to detail.

- Ability to thrive in a fast-paced product development environment.