Machine Learning Engineer (4 Years)

Company Description

Satark AI is an emerging cybersecurity startup headquartered in Gift City Gandhinagar, India, building the worlds 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

-- 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

-- 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

-- Bachelors or Masters 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