Machine Learning Engineer (4+ years)

Company Description

Satark AI is an emerging cybersecurity startup headquartered in Gift City Gandhinagar, India, building the world’s first Autonomous Cyber Leadership Infrastructure.

Satark AI is an AI-powered autonomous cyber intelligence platform that transforms scattered security alerts into clear, context-driven business risk decisions. By correlating signals across tools and organizational environments, Satark AI eliminates up to 70% of security noise and delivers continuous, business-aligned cyber intelligence that helps leaders prioritize real risks and take decisive action. Designed as an always-on cyber leadership layer, Satark AI provides organizations with the clarity, context, and confidence needed to manage cybersecurity as a strategic business function rather than just a technical operation. (https://satark.live/)

Role Description

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.

Fill this form to apply: https://forms.gle/LJNySsPrTwgjLopV6

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

Required Qualifications

Experience

• 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