{"id":2776660,"url":"https://alion.io/job/talentgigs-ai-engineer-cloud-mlops","title":"AI Engineer - Cloud & MLops","company":{"id":4442871,"name":"Talentgigs","domain":"talentgigs.in","url":"https://alion.io/company/talentgigs","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Hyderabad, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":20000,"max_usd":47000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":25},"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Amazon SageMaker","optional":false},{"name":"AutoGen","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock AgentCore","optional":false},{"name":"Azure","optional":false},{"name":"CrewAI","optional":false},{"name":"Embeddings","optional":false},{"name":"Function Calling","optional":false},{"name":"Hallucination","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"OpenAI","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Rest API","optional":false},{"name":"Semantic Kernel","optional":false},{"name":"Tool Use","optional":false},{"name":"Arize Phoenix","optional":true},{"name":"Databricks","optional":true},{"name":"LangSmith","optional":true},{"name":"MLFlow","optional":true},{"name":"Model Context Protocol","optional":true},{"name":"OpenTelemetry","optional":true},{"name":"Structured Outputs","optional":true}],"status":"live","first_seen_at":"2026-09-28T23:30:59Z","employer_posted_date":null,"last_verified_at":"2026-09-28T23:30:59Z","board_verified":false,"closed_at":null,"days_open":19,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":19},"description":"Agentic AI Cloud Engineering / MLOPS Engineer\nBrief Description: This role will be responsible for developing agentic solutions from\nprototype to production, combining LLMs, RAG, tool calling, orchestration frameworks,\ncloud AI services, and modern software engineering practices.\n• 4+ years of experience in AI engineering, software engineering, data engineering,\nML engineering, cloud engineering, or similar technical roles.\n• Hands-on experience building GenAI applications, AI agents, RAG-based\nsolutions, enterprise search, copilots, or LLM-powered workflow\nautomation.\n• Strong programming skills in Python, with experience building APIs, backend\nservices, automation scripts, and reusable AI components.\n• Strong understanding of LLMs, including prompt engineering, context\nengineering, model selection, temperature/top-p settings, context windows,\nembeddings, token usage, latency, and cost trade-offs.\n• Practical experience with RAG architecture, including vector databases,\nembedding models, retrieval strategies, metadata filtering, document\nprocessing, grounding, and citation-based answers.\n• Hands-on experience with multi-agent orchestration patterns, including\nsupervisor-agent architectures, planner-executor workflows, routing agents,\ntool-using agents, evaluator agents, and human-in-the-loop agent flows.\n• Experience implementing tool-calling capabilities, allowing agents to interact\nwith databases, APIs, business applications, documents, and external services.\n• Understanding of agent memory design, including session memory, long-term\nmemory, vector-based memory, user context, conversation history, and\ngoverned memory retention.\n• Experience implementing LLM and agent evaluation frameworks, including\naccuracy testing, grounding validation, hallucination detection, retrieval quality\nassessment, regression testing, adversarial testing, and user feedback\nintegration.\n• Understanding of model governance and responsible AI, including approved\nmodel usage, model selection criteria, evaluation evidence, security controls,\nauditability, and lifecycle management.\n• Experience implementing guardrails for AI agents, including policy-based\ncontrols, restricted tool usage, approval gates, fallback flows, escalation paths,\nhuman-in-the-loop checkpoints, and kill-switch mechanisms.\n• Experience with observability and tracing for agentic systems, including\nexecution traces, tool-call monitoring, prompt/response metadata, token usage,\nlatency, error handling, fallback analysis, and production debugging of multistep workflows.\n• Familiarity with agent development frameworks such as LangChain,\nLangGraph, LlamaIndex, Semantic Kernel, CrewAI, AutoGen, or similar.\n• Experience with cloud-native AI and agentic platforms such as AWS Bedrock\nAgents, AWS Agent Core, AWS SageMaker, Azure OpenAI, Azure AI Agent\nService, Azure AI Foundry, Semantic Kernel, or equivalent technologies.\n• Understanding of enterprise data concepts, including structured data,\nunstructured data, semantic layers, data catalogues, metadata, data\nquality, and governed access.\n• Experience with REST APIs, microservices, authentication, secrets\nmanagement, logging, and cloud-native application patterns.\n• Strong understanding of security and responsible AI principles, including rolebased access, data privacy, prompt injection risks, hallucination control,\ncontent filtering, auditability, and safe agent execution.\n• Ability to work with business stakeholders to understand use cases and translate\nthem into practical AI agent capabilities.\n• Strong communication skills and ability to collaborate with architects, data\nengineers, platform engineers, product owners, and business SMEs.\nNice to have:\n• Experience with agent observability platforms or tracing tools for LLM\napplications, including LangSmith, Arize Phoenix, OpenTelemetry-based tracing,\nMLflow tracing, Databricks MLflow, cloud-native monitoring, or equivalent\nsolutions.\n• Experience designing human-in-the-loop AI systems, including approval\nworkflows, exception management, escalation logic, user feedback capture, and\ncontrolled autonomy.\n• Experience with model risk management, responsible AI, AI governance\nframeworks, prompt governance, model catalogues, evaluation reports, and\naudit-ready documentation.\n• Experience designing tool registries, plugin architectures, MCP-based\nintegrations, OpenAPI-based tools, schema-driven API invocation, and\nreusable agent capabilities.\n• Experience with advanced multi-agent topologies, including supervisor agents,\nplanner-executor agents, critic/evaluator agents, router agents, task-specific\nspecialist agents, and autonomous workflow coordination.\nExperience designing tool registries and schema-driven integrations, using OpenAPI,\nJSON Schema, structured outputs, function-calling definitions, API contracts, and\nvalidation layers","description_format":"text","description_chars":4854,"description_truncated":false,"requirements":{"experience_years_min":4,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":[],"lifecycle":[{"event":"open","at":"2026-10-17T07:43:17Z"}],"visa":[],"liveness":{"score":47,"band":"ok","label":"Likely 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