Ai Ml Engineer

Note: Preference will be given to immediate joiners and candidates currently serving a notice period of up to 20 days.

Virtual interviews will be conducted for out-of-state applicants from 6th to 8th October 2025. Interested candidates are requested to share their updated CVs and availability details via email.

Job Summary:

We are seeking an experienced AI/ML Software Developer with strong hands-on expertise in building agentic AI systems using frameworks like LangGraph, AutoGen, or CrewAI. The ideal candidate will have a deep understanding of Agent-to-Agent collaboration, MCP servers, and Agent/LLM gateway architectures, along with practical experience in fine-tuning language models for domain-specific use cases. A solid grasp of retrieval-augmented generation (RAG), vector databases, knowledge graphs, LangSmith, and AI guardrails is essential.

Key Responsibilities:

• Design, develop, and deploy AI/ML applications with a focus on agent-based architectures.
• Implement and optimize multi-agent collaboration flows using LangGraph, AutoGen, or CrewAI.
• Develop and integrate MCP servers and agent/LLM gateways to enable scalable and robust AI interactions.
• Fine-tune large language models for domain-specific use cases (e.g., healthcare, finance, manufacturing).
• Build and manage RAG pipelines leveraging vector stores (e.g., FAISS, Pinecone, Weaviate) and knowledge graphs.
• Integrate tools like LangSmith for observability and guardrails for responsible AI behavior.
• Work closely with cross-functional teams including product managers, data scientists, and frontend developers to deliver end-to-end solutions.
• Maintain best practices in Python development, testing, and documentation.

Required Qualifications:

• 3+ years of professional software development experience.
• Proficiency in Python, with strong coding and debugging skills.
• Hands-on experience with at least one of the following frameworks: LangGraph, AutoGen, CrewAI.
• In-depth understanding of Agent-based architectures, Agent-to-Agent communication, and LLM orchestration.
• Experience with LLM fine-tuning, prompt engineering, and domain adaptation techniques.
• Knowledge of RAG, vector search engines, knowledge graphs, and relevant open-source tools.
• Familiarity with LangSmith, guardrails frameworks (e.g., GuardrailsAI, PromptGuard), and AI system monitoring tools.

Preferred Qualifications:

• Experience working with cloud-native environments (AWS, Azure, GCP) and containerization tools (Docker, Kubernetes).
• Proficiency with frontend technologies such as React, Angular, or Vue.js is a plus.
• Understanding of MLOps pipelines, CI/CD for AI applications, and LLMOps tools.
• Exposure to multi-modal LLMs and open-source models (e.g., LLaMA, Mistral, Falcon).