Machine Learning Engineer - LLMs and Agentic

Contribute to the design, training, fine-tuning, and deployment of ML/LLM models for production.

Implement RAG pipelines using vector databases.

Work with frameworks like LangChain, LangGraph, MCP to prototype and optimize multi-agent workflows.

Develop prompt engineering, optimization, and safety techniques for agentic LLM interactions.

Integrate memory, evidence packs, and explainability modules into agentic pipelines.

Work hands-on with multiple LLM ecosystems: OpenAI GPT models (GPT-4, GPT-4o, fine-tuned GPTs). Anthropic Claude (Claude 2/3 for reasoning and safety-aligned workflows). Google Gemini (multimodal reasoning, advanced RAG integration). Meta LLaMA (fine-tuned/custom models for domain-specific tasks).

OpenAI GPT models (GPT-4, GPT-4o, fine-tuned GPTs).

Anthropic Claude (Claude 2/3 for reasoning and safety-aligned workflows).

Google Gemini (multimodal reasoning, advanced RAG integration).

Meta LLaMA (fine-tuned/custom models for domain-specific tasks).

Collaborate with Data Engineering to build and maintain real-time and batch data pipelines that serve ML/LLM workloads.

Conduct feature engineering, preprocessing, and embeddings generation for structured and unstructured data.

Implement model monitoring, drift detection, and retraining pipelines.

Leverage cloud ML platforms (AWS Sagemaker, Databricks ML) for experimentation and scaling.

Explore and evaluate emerging LLM/SLM architectures and agent orchestration patterns.

Experiment with generative AI and multimodal models to extend capabilities beyond text (images, structured financial data).

Collaborate with R&D to prototype autonomous resolution agents, anomaly detection models, and reasoning engines.

Translate research prototypes into production-ready components.

Work cross-functionally with R&D, Data Science, Product, and Engineering to deliver business-aligned AI features.

Participate in design reviews, architecture discussions, and model evaluations.

Document processes, experiments, and results effectively for knowledge sharing.

Mentor junior engineers and contribute to ML engineering best practices.