Lead enterprise AI platform development by defining architecture, model-serving strategy, and engineering standards. Build production-grade AI systems, scalable APIs, LLM integrations, RAG pipelines, and agentic workflows. Responsibilities include testing, documentation, deployment, optimization, team mentoring, collaboration, and continuous learning, with ownership and complexity increasing according to experience.
Role Summary
Responsibilities
Focus: Lead enterprise AI platform
development, define architecture, drive model-serving strategy,mentor teams,
establish standards.
Ownership and complexity increase with
experience while retaining focus on building production-grade AI systems, scalable APIs, LLM integrations, RAG pipelines, agentic workflows, testing, documentation, deployment, optimization,
collaboration, and continuous learning.
Strong Python skills appropriate to experience level, backend/API development, AI/LLM exposure,
cloud-native development, and software engineering best practices.
Ownership, code quality, curiosity, speed
with quality, collaboration, and production mindset.
Similar Jobs
Artificial Intelligence • Consumer Web • Digital Media • Information Technology • Social Impact • Software
Lead the AI Quality engineering team while building evaluation infrastructure, observability tooling, datasets, annotation workflows, and diagnostic systems for production AI agents. Design CI/CD evaluation pipelines, run experiments across prompts and models, diagnose agent failures, improve latency and cost, and partner with AI Core engineering. This is a hands-on player-coach role requiring production software experience and expertise in evaluating complex, tool-using LLM systems.
Top Skills:
Ai AgentsBraintrustCi/CdLangsmithLlmsPythonRubyRuby On Rails
Automotive
Build scalable Python backend services, APIs, cloud-native applications, and AI-powered solutions. Develop LLM, RAG, agentic, and multi-agent systems; deploy workloads with Docker and Kubernetes; maintain CI/CD pipelines; and operate applications across major cloud platforms. Collaborate cross-functionally to deliver secure, tested, observable, and production-ready software while optimizing performance, scalability, latency, and cost.
Top Skills:
Amazon Q DeveloperArgocdAWSAzureClaude CodeCursorDockerFaissGitGithub ActionsGithub CopilotGitlab CiGoogle AdkGoogle Cloud PlatformGraphQLHelmJenkinsKubeflowKubernetesLangchainLanggraphLarge Language ModelsMicroservicesMilvusMlflowNosql DatabasesPineconePythonRelational DatabasesRestTerraformTorchserveTriton Inference ServerVllmWeaviateWeights & BiasesWindsurf
Artificial Intelligence • Big Data • Healthtech • Information Technology • Machine Learning • Software • Analytics
Lead end-to-end delivery of enterprise conversational AI platforms, including chatbots, voice bots, and virtual assistants. Design scalable cloud-native solutions using Amazon Lex, Dialogflow, CI/CD, Terraform, and responsible AI practices. Drive Agile execution, stakeholder alignment, operational excellence, and AI adoption while mentoring engineers and technical leads. Partner with product, architecture, security, and business teams to deliver secure, compliant, and innovative AI solutions.
Top Skills:
Agentic AiAmazon ConnectAmazon LexAWSAzureCcaasCi/CdDevOpsDevsecopsFive9GenesysGoogle AdkGCPGoogle DialogflowLlmsNiceNode.jsPythonRagTerraformTypescript
What you need to know about the Chennai Tech Scene
To locals, it's no secret that South India is leading the charge in big data infrastructure. While the environmental impact of data centers has long been a concern, emerging hubs like Chennai are favored by companies seeking ready access to renewable energy resources, which provide more sustainable and cost-effective solutions. As a result, Chennai, along with neighboring Bengaluru and Hyderabad, is poised for significant growth, with a projected 65 percent increase in data center capacity over the next decade.



