Senior AI/ML Software Engineer

About the role

We are seeking a Senior Software Developer with deep AI/ML expertise to design, build, and integrate intelligent applications and AI‑powered solutions. The role works closely with engineers, architects, DevOps/SRE, data teams and business stakeholders to deliver scalable, secure, production‑ready AI services.

Key responsibilities

• Design and develop reliable, maintainable software incorporating Generative AI and Large Language Models.
• Build AI‑powered applications using Retrieval‑Augmented Generation, vector databases, embeddings and semantic search.
• Integrate commercial and open‑source AI models (OpenAI, Anthropic, Gemini, Llama, Mistral, etc.) via APIs and SDKs.
• Implement prompt engineering, context management, evaluation and AI guardrails.
• Develop REST APIs, microservices and backend components in Python and/or Node.js, Java.NET.
• Work with relational, NoSQL and vector databases such as Pinecone, Weaviate, Milvus, pgvector.
• Optimize AI solutions for performance, cost, latency and reliability; implement automated testing frameworks.
• Collaborate with DevOps/MLOps to deploy AI workloads in cloud environments and ensure security, privacy and responsible‑AI compliance.
• Mentor junior developers, conduct code reviews and contribute to architecture decisions.

Required profile

• 7+ years of professional software development experience.
• Strong background in Python and at least one other language (Java, JavaScript/TypeScript, C#, Go).
• Deep understanding of REST APIs, microservices, distributed and event‑driven architectures.
• Hands‑on experience with Generative AI, LLMs, RAG pipelines and AI agents.
• Proven ability to work with both commercial and open‑source AI models.
• Experience designing secure, scalable AI solutions and applying responsible‑AI principles.

Required skills

• Python
• Java
• JavaScript/TypeScript
• C#
• Go
• Node.js
• .NET
• REST APIs
• Microservices
• Distributed systems
• Event‑driven architectures
• Relational databases (SQL)
• NoSQL databases
• Vector databases (Pinecone, Weaviate, Milvus, pgvector)
• Generative AI and Large Language Models (OpenAI, Anthropic, Gemini, Llama, Mistral)
• Prompt engineering
• RAG, embeddings, semantic search