We're hiring an AI Engineer to design, build, and ship AI/ML and generative AI solutions for our client projects — spanning LLM-powered applications, RAG pipelines, and classical ML systems. This is a client-facing delivery role: you'll work directly with international stakeholders to turn business problems into working, production-grade AI systems, not just prototypes. AWS experience is a plus, since our US partner firm is an AWS Advanced Partner, but it is not mandatory — we care more about your ability to reason about AI system architecture across any cloud stack.

Responsibilities

- Design, build, and deploy production-grade AI/ML and generative AI solutions — LLM-powered applications, RAG pipelines, agentic workflows, and classical ML models — for client engagements.

- Work directly with international clients to translate business requirements into technical AI solution designs; contribute to scoping, estimation, and architecture discussions across time zones.

- Build and optimize RAG pipelines: chunking strategies, embeddings, hybrid retrieval, and vector search, along with prompt engineering for accuracy and reliability.

- Integrate LLM APIs (OpenAI, Anthropic, open-source models via Hugging Face, etc.) and orchestration frameworks (LangChain, LlamaIndex, or similar) into client backend and product systems.

- Fine-tune and customize foundation models where needed (SFT, PEFT/LoRA) for domain-specific client use cases.

- Build and maintain data pipelines for training and inference — cleaning, labeling, and feature engineering.

- Deploy and operate models and AI services on cloud infrastructure (AWS, GCP, or Azure) using Docker, Kubernetes, and CI/CD pipelines.

- Set up evaluation harnesses and monitoring for accuracy, hallucination rate, latency, drift, and inference cost; iterate based on production feedback.

- Collaborate with backend, frontend, DevOps, QA, and project management, as well as directly with client teams, across the full delivery lifecycle.





- Review code and architecture, mentor junior engineers, and contribute to reusable AI accelerators and technical standards for the practice.

- Track the fast-moving AI/GenAI landscape and help make pragmatic build-vs-buy calls on AI tooling and infrastructure.

Requirements

- 5+ years of software/ML engineering experience, with meaningful hands-on time building and shipping production AI/ML or generative AI systems (not just research or notebooks).

- Strong, production-quality Python.

- Hands-on experience with at least one deep learning framework (PyTorch or TensorFlow).

- Practical experience building LLM-powered applications — RAG, prompt engineering, and at least one LLM orchestration framework (LangChain, LlamaIndex, or equivalent).

- Experience with vector databases (Pinecone, Weaviate, FAISS, Chroma, or similar).

- Working knowledge of model fine-tuning approaches and their trade-offs.

- Experience deploying ML/AI workloads on at least one major cloud platform (AWS, GCP, or Azure).

- Solid software engineering fundamentals: REST/GraphQL APIs, Docker, CI/CD, Git, automated testing.

- SQL and experience building data/ETL pipelines.

- Strong verbal and written communication — comfortable working directly with international clients across cultures and time zones.

- Bachelor's or Master's degree in Computer Science, AI/ML, Data Science, or a related field, or equivalent practical experience.

Preferences

- AWS experience specifically (SageMaker, Bedrock, Lambda, etc.) — a plus, since our US partner firm holds AWS Advanced Partner status, but not required.

- Experience with agentic AI frameworks and tool-use / multi-step reasoning architectures.

- MLOps tooling (MLflow, Kubeflow, Vertex AI, Azure ML).

- Inference optimization techniques — quantization, batching, speculative decoding.

- Prior experience in an IT services or consulting setting delivering to overseas clients.

- Experience with open-source foundation models (Llama, Mistral, Gemma, etc.).

- Exposure to AI governance, safety, or responsible-AI practices.

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