Senior AI Engineer
Ecolab Inc.2 hours ago
Bengaluru, IndiaSenior
Responsibilities
- Design, develop, test, deploy, and support production-grade LLM-powered applications and AI-enabled services.
- Build and optimize retrieval-augmented generation pipelines covering document ingestion, chunking, embeddings, retrieval, and response grounding.
- Implement agentic AI workflows for task execution, tool usage, context handling, and multi-step orchestration.
- Develop AI-enabled APIs and backend services using Python, FastAPI, Azure Functions, containerized services, and REST integrations.
- Use LLM platforms, AI development tools, orchestration frameworks, vector search platforms, and cloud services to deliver scalable GenAI solutions.
- Integrate AI services with enterprise systems, APIs, workflow platforms, backend systems, and downstream applications.
- Implement logging, tracing, monitoring, and operational controls for deployed AI systems.
- Participate in design reviews, code reviews, testing, release activities, and production troubleshooting.
- Create reusable prompt patterns, orchestration templates, shared components, developer utilities, and GenAI accelerators.
- Improve reliability and support continuous enhancement across the AI application lifecycle.
- Collaborate with AI engineers, architects, product teams, engineers, product owners, and platform teams.
Requirements
- 5 to 8+ years of experience in software engineering, AI/ML engineering, or AI solution delivery, including hands-on intelligent application development and deployment.
- Experience delivering GenAI, LLM-powered, or AI-enabled solutions in development, pilot, or production environments.
- Strong Python and modern backend engineering experience, including APIs, services, and application components.
- Hands-on experience with platforms such as Azure OpenAI, Azure AI Studio, OpenAI API, AWS Bedrock, or Google Vertex AI.
- Experience with orchestration frameworks such as Semantic Kernel, LangChain, or AutoGen for prompt workflows, tool calling, and agent coordination.
- Strong knowledge of RAG, embeddings, vector search, and grounding patterns using platforms such as Azure AI Search, Pinecone, Weaviate, or FAISS.
- Experience deploying cloud-native AI services with Azure Functions, Azure Container Apps, FastAPI, Docker, GitHub, or Azure DevOps.
- Understanding of CI/CD, containerization, automated testing, and secure deployment practices.
- Familiarity with observability tools such as Application Insights, OpenTelemetry, Azure Monitor, Datadog, or New Relic.
- Experience integrating AI services with REST APIs, enterprise workflows, backend systems, or downstream business applications.
- Ability to translate requirements into structured technical implementations and own work across design, build, testing, deployment, support, and improvement.
- Preferred: experience with multi-step agentic workflows, tool orchestration, structured prompting, MCP, A2A interaction patterns, Microsoft AI Foundry, Azure Machine Learning, Azure AI, or Copilot Studio.
- Preferred: experience with enterprise integrations, event-driven architectures, reusable GenAI assets, AI governance, safety, evaluation, cost management, or prompt quality monitoring.
- Working knowledge of TypeScript or C# in addition to Python.
- Strong communication and collaboration skills with technical and non-technical stakeholders.
Tech Stack
Categories