
Senior/Lead Machine Learning Engineer
- Gurgaon, Haryana
- Permanent
- Full-time
- Define architectural patterns for scalable LLM pipelines, ensuring robust versioning, monitoring, and adherence to best practices.
- Drive the integration of external knowledge bases and retrieval systems to augment LLM capabilities.
- Effective RAG architectures and technologies for organizing complex domain-specific data (e.g. vector databases, knowledge graphs) and effective knowledge extraction
- Explore and benchmark state-of-the-art LLMs, tuning, adaptation, and training for performance and cost efficiency.
- Incorporate recent trends like instruction tuning, RLHF, or LoRA fine-tuning for domain customization.
- Embed domain-specific ontologies, taxonomies, and style guides into NLP workflows to adapt models to unique business contexts.
- Analyze models for quality, latency, sustainability metrics, and cost, identifying and implementing improvements for better outcomes.
- Define and own the ML-Ops for your Pod.
- Experimentation and Continuous Improvement:
- Develop experiments for model evaluation and improvement, keeping the solutions aligned with evolving industry standards.
- Establish scalable coding standards and best practices for maintainable and production-ready systems.
- Mentor ML engineers to foster their personal growth.
- Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
- Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
- Minimum 5 years of experience implementing machine learning projects.
- At least 2 years in a senior or lead role.
- Demonstrated expertise integrating modern LLMs into production systems.
- Proven leadership in driving technical projects to successful completion in agile environments
- Strong communication skills to align technical solutions with business goals.
- Ability to mentor and foster innovation within the team.
- LLM and RAG Expertise:
- Strong expertise in building Retrieval-Augmented Generation (RAG) architectures and integrating with vector and graph databases.
- In-depth experience with modern Transformer-based LLMs (e.g., GPT-4, Claude, Gemini, Llama, Falcon, Mistral)
- Demonstrated ability to fine-tune and optimize LLMs for quality, latency, sustainability and cost-effective performance.
- Programming and NLP Tooling:
- Advanced Python proficiency and expertise with frameworks like PyTorch, TensorFlow, Hugging Face, or LangChain.
- MLOps and Deployment:
- Experience with containerization tools (e.g. Docker, Kubernetes) and workflow management tools (e.g. Azure ML Studio, MLFlow)
- Hands-on experience with (preferably Azure) Cloud environments for scalable AI deployment, monitoring, and optimization.
- Document Processing and knowledge extraction tools
- Experience with relational (SQL), NoSQL databases
- Familiarity with platforms like Snowflake or Databricks
- We are curious about other people and their motivations; about new business models and technologies; about each other and the future.
- We are optimistic, focusing on solutions rather than problems; we plan for success and are willing to take calculated risks instead of playing it safe.
- We are ambitious, setting our own standards higher than others' expectations, and we celebrate each other's successes.
- We are empathetic, taking ourselves, others, and each other seriously without prejudgment, and we help each other and our clients, colleagues, and the world.