Senior AI Engineer / Agentic AI Specialist

We are in need of an experienced AI Engineer / Agentic AI Specialist to support our ongoing and upcoming AI-driven initiatives. This role is critical for building and scaling intelligent systems, including agent-based frameworks, RAG pipelines, and enterprise-grade AI integrations.

We are looking for an experienced and highly skilled AI Engineer with hands-on expertise in developing and deploying advanced AI-driven solutions. The ideal candidate will have a strong background in machine learning, LLMs, and agent-based systems, and a passion for delivering scalable and intelligent enterprise solutions.

This role is perfect for someone who thrives in designing autonomous AI agents, integrating with enterprise systems, and leading the charge on emerging AI architectures such as RAG pipelines, Agentic workflows, and MCP orchestration.

Key Responsibilities:
Design, build, and deploy scalable AI/ML models for NLP, computer vision, predictive analytics, or generative AI. Develop agent-based systems using frameworks like LangChain Agents, ChatGPT, etc Architect and maintain RAG pipelines, agent memory, toolchains, and task orchestration flows. Work on end-to-end ML pipelines (data prep, model training, validation, deployment). Integrate with external systems via APIs, vector stores, enterprise tools, and control planes. Collaborate with product managers, engineers, and data scientists to solve business problems with AI. Implement MLOps and LLMOps practices to ensure performance, accuracy, monitoring, and continuous learning. Stay current with cutting-edge research and apply innovative techniques to real-world use cases.

Required Skills Experience:
5+ years of hands-on experience in AI/ML development using Python. Strong foundation in NLP, transformers, and LLMs (e. g. , GPT-4, Claude, LLaMA, Mistral). Proficient with frameworks such as PyTorch, TensorFlow, LangChain, Hugging Face, FastAPI, etc Experience with deploying models on cloud platforms (Azure, AWS, GCP) using Docker, Kubernetes. Familiarity with vector databases (Pinecone, FAISS, Weaviate) and retrieval strategies (RAG). Ability to fine-tune, prompt-engineer, or chain LLMs for business-specific outcomes.

Preferred / Highly Desirable:
Experience with Agentic AI frameworks like LangChain Agents, CrewAI, Semantic Kernel, or AutoGPT. Working knowledge of Multi-Agent Control Platforms (MCP) for orchestration and workflow execution. Understanding of tool use by agents, autonomous task routing, memory management, and real-time decision logic. Exposure to enterprise system integration (e. g. , SAP, Salesforce, ServiceNow, Jira, SharePoint). Familiarity with LLMOps/MLOps tools like MLflow, Prefect, Airflow, or LangGraph. Experience with CI/CD for AI models and scalable architecture design. Strong understanding of responsible AI, data privacy, and model explainability.