Machine Learning Engineer

Job description

REQUIREMENTS:

• Total experience of 6 years+
• Strong expertise in Python and backend engineering with experience building scalable, distributed microservices.
• Hands-on experience designing and delivering end-to-end RAG (Retrieval-Augmented Generation) workflows in production systems.
• Solid understanding of ML solution design, including embeddings, retrieval, ranking, feature engineering, and evaluation strategies.
• Experience with vector databases (FAISS, Pinecone, Milvus, Weaviate) and implementing chunking, indexing, vector search, re-ranking, caching, and memory patterns.
• Knowledge of LLM/NLP engineering, including prompt engineering, model integration, orchestration tools (LangChain/LlamaIndex), and evaluation instrumentation.
• Experience productionizing ML systems with observability, online/offline parity, and performance optimization across latency, throughput, and cost.
• Strong backend integration skills using REST/gRPC APIs, Docker, Kubernetes, CI/CD, and cloud platforms (AWS/GCP/Azure).
• Ability to independently design, ship, and operate reliable, scalable, and cost-efficient ML-backed backend systems with strong ownership mindset.

RESPONSIBILITIES:

• Design and build core backend services powering AI/ML runtime including orchestration, session/state management, and tools/services integration.
• Implement end-to-end retrieval and memory systems covering ingestion, embeddings, indexing, vector search, ranking, caching, and lifecycle management.
• Productionize ML workflows with feature/metadata services, model integration contracts, and evaluation hooks.
• Drive performance, reliability, and cost optimization with strong SLO ownership and observability practices (logs, metrics, tracing, guardrails).
• Collaborate with applied ML teams on model routing, prompts/tools, evaluation datasets, and safe releases.
• Translate business requirements into scalable technical designs, define NFR benchmarks, and review architecture for extensibility and best practices.
• Lead troubleshooting, root-cause analysis, and POCs to validate technology and design decisions.

Qualifications

Bachelors or masters degree in computer science, Information Technology, or a related field.