We are looking for a Senior AI Engineer who can take AI proof-of-concepts and turn them into reliable, scalable, secure, and production-ready applications. The person will work independently, own solutions end to end, and guide the team on AI engineering best practices and new technologies
The Role Will Involve
LLM-based solutions such as APIs, RAG, and AI agents.
Classical Machine Learning and Deep Learning solutions such as forecasting, anomaly detection, and computer vision.
Taking AI solutions from idea/prototype to production.
Providing technical guidance and recommending the right AI technologies and approaches.
Key Responsibilities
Understand business problems and convert them into practical AI use cases with measurable KPIs.
Design enterprise-grade AI solutions that are scalable, secure, reusable, and maintainable.
Choose suitable technologies by considering best practices, cost, risks, benefits, performance, and scalability.
Build complete AI solutions from problem understanding and design to development, testing, deployment, and monitoring.
Develop LLM applications using prompts/system instructions, context, RAG, evaluation, and guardrails.
Develop, train, and deploy ML/DL models whenever required.
Build test frameworks and monitoring to ensure AI solutions perform reliably in production.
Work independently and provide technical direction to other team members.
Required Skills & Qualifications
Software Engineering – Mandatory
Minimum 5 years of software development experience.
Strong programming skills in Python.
Good software engineering practices including Git, code reviews, automated testing, and documentation.
Experience designing and consuming REST APIs.
Experience building APIs/services using FastAPI or a similar framework.
Good knowledge of SQL, databases, and structured data.
Artificial Intelligence – Mandatory
Hands-on experience building and deploying LLM-based applications in a real product or production environment.
Good understanding of LLM model selection, cost optimization, latency, and technical trade-offs.
Strong understanding of AI orchestration frameworks such as LangChain.
Strong understanding of RAG, chunking, vector databases/search, grounding, and guardrails.
Experience building reliable LLM-powered applications using appropriate prompts, context, and evaluation methods.
MLOps / AIOps – Mandatory
Experience taking AI/ML prototypes into production and operating them as monitored and maintainable services.
Hands-on experience with managed LLM platforms in production.
Azure OpenAI / Azure AI Foundry experience is strongly preferred, including GPT-4o and managed embedding models.
Understanding of secure credential and configuration management, such as Managed Identity and Azure Key Vault.
Nice to Have
Experience with Machine Learning algorithms for regression and classification.
Basic knowledge of React and TypeScript
for AI application/interface prototyping.
Experience with Docker and CI/CD pipelines.
Knowledge of Responsible AI, explainability, AI governance, and security.e
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