Description :

- Design and develop end-to-end AI and Agentic AI solutions for complex business problems

- Build autonomous AI agents capable of reasoning, planning, memory management, and tool orchestration

- Implement multi-agent systems for collaborative problem-solving and workflow automation

- Develop and optimize LLM-powered applications using techniques like RAG, fine-tuning, and prompt optimization

- Create scalable ML and AI pipelines for training, inference, evaluation, and monitoring

- Integrate AI agents with enterprise systems, APIs, databases, and external tools

- Ensure production readiness including performance, reliability, security, and cost optimization

- Collaborate closely with product, data engineering, and platform teams

- Mentor junior engineers and contribute to architectural decisions

- Apply Responsible AI principles including explainability, guardrails, and governance

Required Skills & Qualifications :

Core Technical Skills :

- Strong proficiency in Python (mandatory)

- Solid foundation in Machine Learning algorithms, statistics, and data modeling

- Hands-on experience with Deep Learning frameworks (PyTorch, TensorFlow, Keras)

- Strong understanding of data structures, algorithms, and system design

- Experience working with structured and unstructured data

Generative AI & LLMs :

- Hands-on experience with Large Language Models (OpenAI, Azure OpenAI, Anthropic, Llama, Mistral, etc.)

- Experience building RAG pipelines, embeddings, and vector search

- Familiarity with vector databases (FAISS, Pinecone, Weaviate, Chroma)

- Prompt engineering, evaluation, and fine-tuning techniques

Agentic AI (Mandatory/Strongly Preferred) :

- Proven experience designing and implementing Agentic AI systems

- Hands on experience with AI agent frameworks such as :

- LangGraph/LangChain Agents

- AutoGen

- CrewAI

- Semantic Kernel (Agents)

- Ability to design agents with :

- Tool calling and function execution

- Long-term and short-term memory

- Planning, reflection, and self-correction

- Human-in-the-loop workflows

- Experience with multi-agent coordination, task delegation, and orchestration

- Knowledge of agent evaluation, observability, and guardrails

MLOps & Deployment :

- Experience deploying AI models and agents in production environments

- Strong knowledge of MLOps practices (MLflow, Kubeflow, Airflow, SageMaker, Vertex AI, etc.)

- Experience with Docker, Kubernetes, and CI/CD pipelines

- API development using REST or gRPC

- Monitoring, logging, and model performance tracking

Cloud & Platforms :

- Hands-on experience with AWS, Azure, or GCP

- Experience integrating AI solutions into enterprise applications and workflows

Educational Qualifications :

- Bachelors or masters degree in computer science, Engineering, AI, Data Science, Mathematics, or related field

- Relevant AI/ML or cloud certifications are a plus

Soft Skills :

- Strong analytical and problem-solving abilities

- Ability to explain complex AI concepts to business stakeholders

- Ownership mindset with the ability to lead technically

- Strong collaboration and mentoring skills