AI Engineer

About the Role:

We are seeking a passionate and innovative Gen AI Engineer / Senior Engineer / Lead with hands-on experience in building and deploying Agentic AI systems. As a core member of our team, you will design, implement, and scale intelligent agents that autonomously plan, execute and refine tasks across diverse enterprise use cases.

AI Engineers with 5+ years of expertise in designing, developing and implementing advanced AI models and algorithms, with a strong focus on Generative AI, Retrieval-Augmented Generation (RAG), context engineering.

If you are excited about shaping the future of autonomous AI solutions and have experience in at least one cloud platform, wed love to hear from you!

Key Responsibilities

• Design and develop Agentic AI architectures using LLMs, planning modules, memory stores, and tool integration frameworks.
• Implement task orchestration, decision reasoning, and self-reflection capabilities within autonomous agents.
• Work with multi-agent workflows involving planning, tool use, dialogue, and adaptive learning loops.
• Integrate and fine-tune LLMs (GPT, Llama, Claude, etc.) for use in agent workflows.
• Collaborate with product and platform teams to deploy solutions on cloud environments (AWS, Azure, GCP).
• Contribute to solution accelerators, reusable frameworks, and reference architectures for Gen AI use cases.
• Optionally contribute to MLOps/LLMOps pipelines for continuous model integration, evaluation, and monitoring.

• Minimum 5 years to 7 years in AI/ML development with strong Python skills.
• Atleast 5+ years of experience in AI/ML/GenAI domains, with at least 2 year of experience in Generative AI.
• Strong understanding of LLM architectures, Agentic AI, prompt engineering, RAG pipelines, and memory systems (e.g., vector DBs).
• Experience in building agents using frameworks such as LangChain, AutoGen, CrewAI, Semantic Kernel, or similar.
• Proficiency in Python and relevant Gen AI libraries (Transformers, HuggingFace, OpenAI API, etc.).
• Experience deploying solutions on at least one cloud platform (AWS, Azure, or GCP).
• Strong problem-solving skills and ability to work in a fast-paced, agile environment.
• Strong Python programming skills

• Experience with MLOps or LLMOps pipelines (e.g., MLflow, Kubeflow, SageMaker, Azure ML, Weights & Biases).
• Knowledge of secure deployment, prompt injection prevention, and observability in Gen AI systems.
• Familiarity with Open-Source LLM hosting and model serving frameworks.