AI Engineer
Indexed description
We are seeking engineers who combine strong software engineering fundamentals with expertise in modern AI technologies. The ideal candidate should be capable of building production-ready AI systems, engaging directly with customers, and delivering innovative AI solutions using the latest advancements in Generative AI. Experience working on enterprise AI projects, strong communication skills, and the ability to own projects end-to-end are essential for success in this role.
Responsibilities
- Design, develop, and deploy enterprise-scale AI applications using Large Language Models (LLMs) and Generative AI technologies.
- Collaborate directly with customers to understand business requirements and translate them into scalable AI solutions.
- Lead technical discussions, solution architecture workshops, and proof-of-concept (POC) engagements.
- Build and optimise Retrieval-Augmented Generation (RAG) pipelines for enterprise use cases.
- Develop AI Agents and Agentic AI workflows for process automation and intelligent decision-making.
- Integrate AI models with enterprise applications, APIs, databases, and cloud platforms.
- Develop scalable backend services using modern Python frameworks and microservice architecture.
- Deploy AI applications in production environments with proper monitoring, security, and performance optimisation.
- Continuously evaluate and adopt the latest AI technologies, frameworks, and best practices.
- Strong expertise in Python.
- Experience building scalable backend services using FastAPI.
- Strong understanding of REST APIs, Microservices, Async Programming, and API integrations.
- Hands-on experience with Large Language Models (LLMs)
- Experience working with OpenAI, Claude, Gemini, Llama, Mistral, Qwen, or similar foundation models
- Prompt Engineering and Function Calling
- Structured Outputs and Tool Calling
- AI Agents / Agentic AI
- Strong experience designing and implementing RAG solutions.
- LangChain
- LangGraph
- LlamaIndex
- PydanticAI or similar AI orchestration frameworks.
- Experience with Model Context Protocol (MCP) is an added advantage.
- Vector Databases: Experience with one or more: Pinecone, Weaviate, Milvus, Qdrant, FAISS, ChromaDB.
- PyTorch or TensorFlow.
- Hugging Face Transformers.
- Model evaluation and optimisation.
- Fine-tuning techniques such as LoRA or PEFT (preferred).
- Google Cloud Platform (Vertex AI preferred)
- AWS (Amazon Bedrock)
- Microsoft Azure (Azure AI Foundry)
- Docker
- Kubernetes
- CI/CD
- GPU-based inference (preferred)
- PostgreSQL
- MongoDB
- Redis
- Work closely with enterprise customers to gather functional and technical requirements.
- Present AI solution designs, architecture, and implementation approaches.
- Build and demonstrate AI proofs-of-concept (POCs).
- Support customer deployments and production rollouts.
- Act as a trusted technical advisor throughout the project lifecycle.
- Communicate effectively with engineering teams, business stakeholders, and leadership.
- Google (Gemini, Vertex AI)
- NVIDIA (NIM, TensorRT-LLM, Triton Inference Server)
- Meta (Llama ecosystem)
- OpenAI
- Anthropic
- Microsoft Azure AI
- AWS Bedrock
- Enterprise AI Assistants
- AI Copilots
- Agentic AI Applications
- Intelligent Document Processing
- Enterprise Search Platforms
- Multi-Agent Systems
- AI-powered SaaS Products
- Excellent verbal and written communication skills.
- Strong customer-facing and consulting experience.
- Ability to confidently present technical solutions to clients and leadership teams.
- Strong analytical and problem-solving skills.
- Self-driven with the ability to work independently and manage multiple priorities.
- Passion for learning and adopting the latest advancements in AI.
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related field.
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