Principal Engineer, Agentic AI

REQUIREMENTS:

• Total experience 13 years.
• Should have experience to architect and design scalable autonomous AI systems with a focus on Generative AI agentic frameworks and foundation models
• Should be able to lead the evaluation and integration of open-source and proprietary LLMs (e.g. GPT Claude LLaMA Mistral).
• Should be able to define architectures for autonomous agents multimodal models (text image audio) Retrieval-Augmented Generation (RAG) systems and natural language to SQL pipelines.
• Should be able to collaborate with product research and engineering teams to align AI initiatives with business strategy.
• Should have hands-on experience with frameworks such as Hugging Face Transformers LangChain OpenAI API or similar.
• Deep understanding of agent orchestration frameworks (e.g. AutoGen CrewAI LangGraph) LLM fine-tuning (e.g. LoRA PEFT) RAG pipelines and vector databases (e.g. FAISS Pinecone).
• Should be able to design solutions leveraging cloud-native architecture (AWS GCP Azure) with emphasis on GPU infrastructure.
• Must have experience to develop modular goal-driven AI systems capable of task planning memory persistence context tracking and multi-agent collaboration.
• Should have experience building autonomous AI agents using frameworks such as AutoGPT LangGraph CrewAI or ReAct.
• Strong understanding of task decomposition memory management and tool augmentation in agentic workflows.
• Should have familiarity with long-context memory techniques and multi-agent coordination strategies.
• Must have hands-on experience with agent simulations LLM-driven planning and dynamic tool selection.
• Should have knowledge of how to embed feedback reasoning loops (e.g. Reflexion Chain-of-Thought) and self-correction mechanisms into GenAI agents.
• Must have experience with human-in-the-loop (HITL) oversight and reinforcement learning for autonomous systems.
• Exhibit excellent communication leadership and collaboration skills.

RESPONSIBILITIES:

• Understanding the clients business use cases and technical requirements and be able to convert them into technical design which elegantly meets the requirements.
• Must be able to establish best practices for fine-tuning prompt engineering model compression and inference optimization.
• Should be able to Implement robust MLOps practices for model versioning deployment monitoring and governance.
• Address ethical AI concerns including bias hallucination and data privacy in generative applications.
• Provide technical leadership in building and deploying generative models (LLMs diffusion models transformers etc.).
• Ensure the responsible deployment of autonomous agents through safety layering feedback loops and auditability mechanisms.
• Mapping decisions with requirements and be able to translate the same to developers.
• Identifying different solutions and being able to narrow down the best option that meets the clients requirements.
• Defining guidelines and benchmarks for NFR considerations during project implementation.
• Writing and reviewing design document explaining overall architecture framework and high-level design of the application for the developers.
• Reviewing architecture and design on various aspects like extensibility scalability security design patterns user experience NFRs etc. and ensure that all relevant best practices are followed.
• Developing and designing the overall solution for defined functional and non-functional requirements; and defining technologies patterns and frameworks to materialize it.
• Understanding and relating technology integration scenarios and applying these learnings in projects.
• Resolving issues that are raised during code/review through exhaustive systematic analysis of the root cause and being able to justify the decision taken.
• Carrying out POCs to make sure that suggested design/technologies meet the requirements.

Qualifications :

Bachelors or masters degree in computer science Information Technology or a related field.

Additional Information :

Work :

Yes

Employment Type :

Full-time