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