Machine learning engineer

Job Responsibilities

Development and integration of Python-based applications with LLMs (Open AI, Deep Seek, Anthropic, LLa MA, etc.).

Architect and implement LLM pipelines including prompt engineering, retrieval-augmented generation (RAG), fine-tuning, and evaluation. Design scalable microservices and APIs for AI features.

Collaborate with MLOps teams to deploy and monitor AI models in production. Ensure performance optimization, cost efficiency, and security in LLM workflows.

Guide the team on Python best practices, code reviews, and technical problem-solving. Stay updated on emerging AI / LLM advancements and propose adoption where beneficial.

Mandatory Skills

Strong proficiency in Python (Fast API, Flask). Solid experience with LLM integration (Open AI API, Hugging Face Transformers, Lang Chain, Llama Index). Understanding of RAG pipelines (vector databases like Pinecone, Weaviate, FAISS, Milvus). Experience with prompt engineering & evaluation techniques. Knowledge of MLOps tools (MLflow),

Kubeflow, Langfuse) and deployment on AWS / GCP / Azure. Familiarity with containerization and orchestration (Docker, Kubernetes). Strong grasp of REST APIs, Graph QL, and microservices architecture. Knowledge of model fine-tuning and performance optimization.

Familiarity with containerization and orchestration (Docker, Kubernetes). Strong grasp of REST APIs, Graph QL, and microservices architecture. Knowledge of model fine-tuning and performance optimization.

Optional Skills

Excellent leadership and mentoring abilities. Strong problem-solving and analytical skills.

Effective communication with technical and non-technical stakeholders.

Ability to work in a fast-paced, evolving AI environment.

Experience with agentic AI or Model Context Protocol (MCP).

Background in data pipelines (ETL, streaming data).

Exposure to AI security & compliance practices. Prior to work in scalable enterprise AI products.