Senior Gen AI Engineer

We are seeking a highly skilled Senior Gen-AI & AI/ML Engineer to design, build, and deploy intelligent agentic systems that solve complex, real-world problems at enterprise scale. You will work on cutting-edge AI frameworks, multimodal pipelines, MCP-based infrastructures, and agent-driven workflows that combine autonomous reasoning with human-in-the-loop learning. This role is ideal for engineers who enjoy hands-on model development, production-grade deployments, and building advanced AI capabilities that directly impact business outcomes.

Role and responsibilities:

Agentic & AI Systems Development

• Design and deploy intelligent, agent-driven systems that autonomously solve complex business problems using advanced AI algorithms.
• Engineer collaborative multi-agent frameworks capable of coordinated reasoning and action for large-scale applications.
• Build and extend MCP-based infrastructure enabling secure, context-rich interactions between agents and external tools/APIs.

Human-in-the-Loop AI

• Develop workflows that combine agent autonomy with human oversight, enabling continuous learning through feedback loops (e.g., RLHF, in-context correction).

AI/ML Engineering

• Build, fine-tune, train, and evaluate ML and deep-learning models using frameworks such as PyTorch and TensorFlow.
• Work with multimodal data pipelines (text, images, structured data) for embedding generation, feature extraction, and downstream tasks.
• Integrate models into production systems via APIs, inference pipelines, and monitoring tools.

Engineering Excellence

• Use Git, testing frameworks, and CI/CD processes to ensure high-quality, maintainable code.
• Document architectural decisions, trade-offs, and system behavior to support collaboration across teams.
• Stay updated with AI research trends and apply relevant advancements into product design.

Core Technical Skills:

• Strong fluency in Python and Agentic frameworks.
• Solid understanding of ML fundamentals: optimization, representation learning, evaluation metrics, supervised/unsupervised/generative modeling.
• Hands-on experience with multimodal datasets and feature pipelines.
• Experience deploying ML models to production, including inference optimization and monitoring.
• Familiarity with LLMOps/MLOps concepts: versioning, reproducibility, observability, governance.

Bonus Skills (Great to Have):

• • Experience designing goal-oriented agentic systems and multi-agent coordination workflows.
  • Proficiency with LangChain, LangGraph, AutoGen, Google ADK or similar agent orchestration frameworks.
  • Knowledge of secure tool/agent communication protocols such as MCP.
  • Exposure to reinforcement learning from human feedback (RLHF) and reward modeling.
  • Cloud experience