Role Overview:

We are looking for an experienced Forward Deployed AI Engineer to design, build, and deploy AI-powered solutions that address real-world business challenges. The role combines software engineering, Generative AI, system integration, and direct client collaboration.

Key Responsibilities:

- Work directly with client stakeholders to understand business requirements, identify high-impact AI use cases, and translate them into practical technical solutions.

- Design, develop, and deploy AI-powered applications using Python, Large Language Models (LLMs), and modern AI frameworks.

- Build and integrate Generative AI solutions using prompt engineering, Retrieval-Augmented Generation (RAG), embeddings, vector databases, and agentic workflows.

- Develop backend services, REST APIs, and data pipelines to integrate AI capabilities with existing enterprise applications and systems.

- Build rapid prototypes and proof-of-concept solutions, validate technical feasibility, and evolve successful prototypes into production applications.

- Integrate enterprise data sources, APIs, databases, and third-party platforms to enable AI-powered workflows and business process automation.

- Evaluate LLMs, AI frameworks, retrieval strategies, and orchestration tools to improve solution accuracy, relevance, performance, and reliability.

- Deploy and maintain AI applications on cloud platforms, ensuring scalability, security, observability, and operational stability.

- Implement testing, evaluation, monitoring, and guardrails to assess AI outputs, manage failure scenarios, and minimise hallucinations and other risks.

- Optimise AI applications for latency, token usage, inference costs, throughput, and resource utilisation.

- Collaborate with client engineering teams, product managers, data engineers, and other stakeholders to drive implementation and adoption.

- Conduct technical workshops, demonstrate solutions, communicate trade-offs, and explain technical concepts in terms of business outcomes.

- Troubleshoot issues across application, data, integration, and AI layers, performing root cause analysis and implementing improvements.

- Document solution architectures, implementation decisions, integration patterns, and deployment procedures.

- Stay informed about emerging AI capabilities and identify opportunities to improve client solutions through experimentation and innovation.

Required Skills & Experience:

- 5 - 8 years of relevant experience in software engineering, AI engineering, machine learning engineering, or application development.

- Strong hands-on proficiency in Python, including backend development and API integration.

- Practical experience building and deploying Generative AI applications using LLMs.

- Experience implementing RAG pipelines, prompt engineering, embeddings, and vector search.

- Understanding of agentic AI systems, tool calling, workflow orchestration, and multi-step AI applications.

- Experience developing REST APIs, integrating enterprise systems, and working with databases and external data sources.

- Familiarity with AI frameworks and tools such as LangChain, LangGraph, LlamaIndex, or equivalent technologies.

- Experience with cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform.

- Understanding of production deployment practices, CI/CD pipelines, automated testing, monitoring, and application troubleshooting.

- Knowledge of AI evaluation, output validation, data privacy, access control, and security considerations for enterprise AI applications.

- Ability to translate ambiguous business problems into technical requirements and deliver working solutions independently.

- Strong communication and stakeholder management skills, including the ability to work directly with clients and conduct technical demonstrations.

- Strong analytical and problem-solving skills, with a practical, delivery-oriented approach to engineering.