AI/ML Engineer

Required Skills & Qualifications

• Bachelor's or master's degree in computer science, Engineering, Data Science, Mathematics, Statistics or related fields.

• At least 3 years of professional experience in AI/ML engineering, with a track record of delivering production-grade AI systems.

• Strong programming skills in Python and SQL, with hands-on proficiency in standard ML/data libraries (scikit-learn, pandas, numpy) and deep learning frameworks (PyTorch or TensorFlow); solid understanding of REST API design and integration patterns.

• Demonstrated hands-on experience building and deploying production-grade ML/LLM systems including RAG pipelines, document parsing, information extraction, and text processing with practical experience handling large-scale unstructured datasets including preprocessing, chunking, and feature engineering.

• Proficiency in cloud-based AI/ML services across AWS and Azure; including AWS SageMaker, Bedrock, and Azure AI Foundry / Azure OpenAI Service; for model training, fine-tuning, deployment, and inference at scale.

• Strong hands-on experience in NLP and extraction-focused ML , including transformers, embeddings, vector databases, RAG, LLM integrations, and agentic workflows with a solid understanding of how to apply these techniques to real-world production systems.

• Experience leading projects or teams, managing technical deliverables, and ensuring high-quality outcomes; strong analytical and problem-solving capabilities with the ability to navigate ambiguous technical challenges.

• Ability to design and use multi-agentic coding frameworks and orchestration tools (e.g. Claude Code, LangGraph, LangChain, CrewAI) for building and managing LLM-based agentic workflows.

• Solid grounding in Machine Learning and Deep Learning; including supervised/unsupervised learning, CNNs, RNNs, Transformers, NLP, and fine-tuning of pre-trained LLMs for domain-specific tasks; experience with evaluation frameworks (precision, recall, F1, field-level accuracy) and iterative model improvement.

• Hands-on experience with MLOps practices; including experiment tracking (MLflow, Weights & Biases), model versioning, automated retraining pipelines, model registry management, and continuous iterative improvement in production.

• Experience with CI/CD pipelines for AI/ML workflows using GitHub Actions, Azure DevOps, or similar; containerization with Docker and orchestration with Kubernetes.

• Proficiency in AI observability and monitoring using Prometheus and Grafana, including setting up dashboards, alerts, and SLOs for production model performance.

• Familiarity with data pipeline and orchestration tools such as Apache Kafka, Apache Airflow, or similar technologies for managing high-throughput data flows.

• Effective communication and collaboration skills, with experience working cross-functionally with product, engineering, and data teams in fast-paced, data-driven environments.

• Relevant certifications are a strong plus including AWS (Solutions Architect, Machine Learning Specialty), Azure (AI Fundamentals, AI Engineer Associate), and Databricks (Machine Learning Professional, Generative AI Engineer Associate).

Pay: ₹410,000.00 - ₹1,700,000.00 per year

Work Location: In person