AI Engineer - Cloud & MLops

Talentgigs
Department Icon Data Science Analytics & Machine Learning
Posted: 3 hours ago
4-6 years
Hyderabad / Secunderabad, Telangana, Telangana
Data Science Analytics & Machine Learning
work from office
Full Time
Closes 13 Dec 2026
Posted: 3 hours ago
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Job Description

  • Agentic AI Cloud Engineering / MLOPS Engineer
  • Brief Description: This role will be responsible for developing agentic solutions from
  • prototype to production, combining LLMs, RAG, tool calling, orchestration frameworks,
  • cloud AI services, and modern software engineering practices.
  • • 4+ years of experience in AI engineering, software engineering, data engineering,
  • ML engineering, cloud engineering, or similar technical roles.
  • • Hands-on experience building GenAI applications, AI agents, RAG-based
  • solutions, enterprise search, copilots, or LLM-powered workflow
  • automation.
  • • Strong programming skills in Python, with experience building APIs, backend
  • services, automation scripts, and reusable AI components.
  • • Strong understanding of LLMs, including prompt engineering, context
  • engineering, model selection, temperature/top-p settings, context windows,
  • embeddings, token usage, latency, and cost trade-offs.
  • • Practical experience with RAG architecture, including vector databases,
  • embedding models, retrieval strategies, metadata filtering, document
  • processing, grounding, and citation-based answers.
  • • Hands-on experience with multi-agent orchestration patterns, including
  • supervisor-agent architectures, planner-executor workflows, routing agents,
  • tool-using agents, evaluator agents, and human-in-the-loop agent flows.
  • • Experience implementing tool-calling capabilities, allowing agents to interact
  • with databases, APIs, business applications, documents, and external services.
  • • Understanding of agent memory design, including session memory, long-term
  • memory, vector-based memory, user context, conversation history, and
  • governed memory retention.
  • • Experience implementing LLM and agent evaluation frameworks, including
  • accuracy testing, grounding validation, hallucination detection, retrieval quality
  • assessment, regression testing, adversarial testing, and user feedback
  • integration.
  • • Understanding of model governance and responsible AI, including approved
  • model usage, model selection criteria, evaluation evidence, security controls,
  • auditability, and lifecycle management.
  • • Experience implementing guardrails for AI agents, including policy-based
  • controls, restricted tool usage, approval gates, fallback flows, escalation paths,
  • human-in-the-loop checkpoints, and kill-switch mechanisms.
  • • Experience with observability and tracing for agentic systems, including
  • execution traces, tool-call monitoring, prompt/response metadata, token usage,
  • latency, error handling, fallback analysis, and production debugging of multistep workflows.
  • • Familiarity with agent development frameworks such as LangChain,
  • LangGraph, LlamaIndex, Semantic Kernel, CrewAI, AutoGen, or similar.
  • • Experience with cloud-native AI and agentic platforms such as AWS Bedrock
  • Agents, AWS Agent Core, AWS SageMaker, Azure OpenAI, Azure AI Agent
  • Service, Azure AI Foundry, Semantic Kernel, or equivalent technologies.
  • • Understanding of enterprise data concepts, including structured data,
  • unstructured data, semantic layers, data catalogues, metadata, data
  • quality, and governed access.
  • • Experience with REST APIs, microservices, authentication, secrets
  • management, logging, and cloud-native application patterns.
  • • Strong understanding of security and responsible AI principles, including rolebased access, data privacy, prompt injection risks, hallucination control,
  • content filtering, auditability, and safe agent execution.
  • • Ability to work with business stakeholders to understand use cases and translate
  • them into practical AI agent capabilities.
  • • Strong communication skills and ability to collaborate with architects, data
  • engineers, platform engineers, product owners, and business SMEs.
  • Nice to have:
  • • Experience with agent observability platforms or tracing tools for LLM
  • applications, including LangSmith, Arize Phoenix, OpenTelemetry-based tracing,
  • MLflow tracing, Databricks MLflow, cloud-native monitoring, or equivalent
  • solutions.
  • • Experience designing human-in-the-loop AI systems, including approval
  • workflows, exception management, escalation logic, user feedback capture, and
  • controlled autonomy.
  • • Experience with model risk management, responsible AI, AI governance
  • frameworks, prompt governance, model catalogues, evaluation reports, and
  • audit-ready documentation.
  • • Experience designing tool registries, plugin architectures, MCP-based
  • integrations, OpenAPI-based tools, schema-driven API invocation, and
  • reusable agent capabilities.
  • • Experience with advanced multi-agent topologies, including supervisor agents,
  • planner-executor agents, critic/evaluator agents, router agents, task-specific
  • specialist agents, and autonomous workflow coordination.
  • Experience designing tool registries and schema-driven integrations, using OpenAPI,
  • JSON Schema, structured outputs, function-calling definitions, API contracts, and
  • validation layers

Skills

PythonData PrivacyPrompt EngineeringAiMlLlmLlmsGenaiRagEmbeddingsTool CallingOpenaiAzure OpenaiGuardrailsLangchainLanggraphLlamaindexAutogenCrewaiSemantic KernelAi AgentAi AgentsAgenticAgentic AiMcpMlopsMlflowSagemakerAws BedrockDatabricksAi Engineer

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