Machine Learning Engineer (4+ yrs)

We are seeking an experienced Machine Learning Engineer to join our team and drive the development of production-grade AI systems. This role requires deep expertise in Retrieval-Augmented Generation (RAG), natural language to SQL conversion, and large language model fine-tuning. You will be responsible for building, deploying, and maintaining sophisticated ML systems that power our products.

Key Responsibilities

Design and implement production-ready RAG pipelines for knowledge-intensive applications

Build and optimize text-to-SQL engines that translate natural language queries into executable SQL code

Develop, train, and fine-tune smaller language models for specific domain applications

Fine-tune large language models (LLMs) using various techniques including supervised fine-tuning, RLHF, and parameter-efficient methods

Deploy and maintain ML models in production environments with monitoring, versioning, and continuous improvement

Optimize model performance, latency, and cost for real-world applications

Collaborate with cross-functional teams to integrate ML capabilities into products

Establish best practices for ML ops, model evaluation, and deployment pipelines

Stay current with latest developments in LLMs, RAG architectures, and ML tooling

Experience

Required Qualifications

4–5 years of hands-on experience in machine learning engineering

Proven track record of building and deploying ML systems in production environments

Experience with the full ML lifecycle from data preparation to production deployment

Technical Skills

RAG & Information Retrieval

Deep understanding of RAG architecture and implementation

Experience with vector databases (Pinecone, Weaviate, Chroma, FAISS, etc.)

Knowledge of embedding models and semantic search techniques

Experience with chunking strategies, retrieval optimization, and context management

Text-to-SQL Systems

Strong experience building natural language to SQL conversion systems

Understanding of database schemas, query optimization, and SQL dialects

Experience with few-shot prompting and query validation techniques

LLM Development & Fine-tuning

Hands-on experience fine-tuning LLMs (GPT, Llama, Mistral, etc.)

Knowledge of fine-tuning techniques: full fine-tuning, LoRA, QLoRA, prefix tuning

Experience training smaller language models from scratch or adapting existing ones

Understanding of model quantization, distillation, and compression techniques

Production ML Systems

Strong software engineering skills with production-level code quality

Experience with ML ops tools and practices (MLflow, Weights & Biases, etc.)

Knowledge of containerization (Docker, Kubernetes) and cloud platforms (AWS, GCP, Azure)

Experience with API development and deployment (FastAPI, Flask, etc.)

Understanding of monitoring, logging, and debugging production ML systems

Programming & Tools

Expert-level Python programming

Proficiency with PyTorch or TensorFlow

Experience with Hugging Face Transformers, LangChain, or LlamaIndex

Familiarity with SQL and database technologies

Version control with Git and collaborative development workflows

Additional Requirements

Strong problem-solving and analytical skills

Excellent communication skills and ability to explain complex technical concepts

Experience working in agile development environments

Bachelor's or Master's degree in Computer Science, Machine Learning, or related field (or equivalent practical experience)

Preferred Qualifications

Experience with prompt engineering and advanced prompting techniques

Knowledge of reinforcement learning from human feedback (RLHF)

Familiarity with evaluation frameworks for LLM applications

Experience with data annotation and synthetic data generation

Publications or contributions to open-source ML projects

Experience with A/B testing and experimentation in production

Understanding of model safety, bias mitigation, and responsible AI practices