Designs and maintains a centralized AI platform and agentic framework supporting secure deployment, management, and integration of AI agents. Responsibilities include building scalable agentic solutions, developing unified APIs, integrating agents into applications, optimizing cloud-native workloads, ensuring security and compliance, conducting capacity planning, mentoring engineers, and shaping the platform roadmap.
Overview
Cotiviti is seeking an experienced AI Engineer to design, build, and maintain a cutting-edge agentic framework and centralized AI platform that will serve as the backbone of our organization's AI initiatives. This role will be responsible for creating a unified interface that enables teams across the company to securely deploy, manage, and access AI agents while ensuring seamless data integration and platform reliability. The role will work closely with Data Scientists, MLOps engineers, Data Engineers, and Product Engineering teams.
Responsibilities- Design and implement scalable agentic solutions for diverse use cases
- Leverage a broad stack of Open Source AI technologies to build and maintain a centralized AI platform with unified APIs for cross-functional team access and build various types of agents
- Work with Product Engineers to integrate AI agents into customer-facing applications and internal tools
- Lead technical discussions, mentor junior engineers, and help set the technical vision for the AI platform roadmap.
- Ensure compliance with security, privacy, and regulatory requirements across the software development lifecycle.
- Optimize performance of AI-powered workloads across compute and storage layers using cloud-native and open-source solutions.
- Conduct performance analysis and capacity planning to ensure platform scalability.
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
- 8+ years of software engineering experience, including 4+ years working on AI/ML platforms or infrastructure.
- Experience in building large-scale distributed systems and microservices.
- Strong programming skills in Python.
- Hands-on experience with agentic AI frameworks such as LangChain, LangGraph, CrewAI, AutoGen, or similar.
- Experience with containerization and orchestration (e.g., Docker, Kubernetes).
- Understanding of MLOps tools such as MLflow, Kubeflow, SageMaker, Vertex AI, or Databricks
- Cloud platform experience (AWS, GCP, or Azure).
- Worked on low-code automation platforms such as n8n, Zapier, Copilot Studio, and similar tools.
Similar Jobs
Artificial Intelligence • Consumer Web • Edtech • Enterprise Web • HR Tech • Social Impact • Generative AI
Owns the enterprise architecture and hands-on implementation of Coursera’s agentic AI foundation. Designs agent orchestration, context and semantic architectures, governance, security, CI/CD automation, cost optimization, integrations, observability, and production standards. Enables business teams to build reliable AI-native workflows at scale while partnering with Engineering, IT, Data, Security, and business pods. The role also includes hiring, coaching, technical leadership development, and defining operating models and adoption metrics.
Top Skills:
Agentic AiAPIsCi/CdCloud InfrastructureLanggraphLlmsModel Context Protocol (Mcp)RagSemantic Search
Artificial Intelligence • Big Data • Logistics • Machine Learning • Software • Transportation
Own the reference architecture and platform for production AI agents, including agent execution, tool use, state management, recovery, context engineering, model routing, guardrails, and human handoffs. Build shared retrieval, memory, and evaluation infrastructure for offline and online quality measurement. Integrate agents with event-driven systems and customer APIs, review designs, mentor engineers, and connect technical metrics to supply chain business outcomes.
Top Skills:
APIsAWSBertClaudeGCPGeminiGptJavaKafkaKubernetesLlmsMcpMicroservicesOcrPostgresPythonTypescript
Information Technology • Business Intelligence • Consulting
Design and implement enterprise generative and multi-agent AI solutions from proof of concept through production. Build agent architectures using Python, LangChain, LangGraph, CrewAI, RAG, LLMs, and cloud technologies. Integrate AI with enterprise applications, APIs, databases, and tools while establishing security, governance, observability, evaluation, and LLMOps practices. Lead architecture reviews and proofs of concept, make technical decisions, and mentor AI engineering teams.
Top Skills:
APIsAutogenAWSAzureCrewaiDockerEmbeddingsGCPGenerative AiGraph DatabasesHugging FaceKnowledge GraphsKubernetesLangchainLangfuseLanggraphLangsmithLlm ApisLlmopsMcpMicroservicesMlflowMlopsMulti-Agentic AiOpen-Source LlmsOpentelemetryPrompt EngineeringPythonRagSemantic KernelTool/Function CallingVector Databases
What you need to know about the Delhi Tech Scene
Delhi, India's capital city, is a place where tradition and progress co-exist. While Old Delhi is known for its rich history and bustling markets, New Delhi is defined by its modern architecture. It's clear the region places a strong emphasis on preserving its cultural heritage while embracing technological advancements, particularly in artificial intelligence, which plays a central role in shaping the city's tech landscape, fueled by investments in research and development.
