{"id":2920127,"url":"https://alion.io/job/ensylon-senior-intergration-ai-engineer","title":"Senior Intergration AI Engineer","company":{"id":4452527,"name":"Ensylon","domain":"ensylon.com","url":"https://alion.io/company/ensylon","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":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Pune, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":21000,"max_usd":44000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":41},"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Amazon ECS","optional":false},{"name":"Amazon S3","optional":false},{"name":"Anthropic","optional":false},{"name":"Apache Kafka","optional":false},{"name":"AutoGen","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"AWS Lambda","optional":false},{"name":"Azure","optional":false},{"name":"CrewAI","optional":false},{"name":"Docker","optional":false},{"name":"ETL/ELT","optional":false},{"name":"FastAPI","optional":false},{"name":"GCP","optional":false},{"name":"GDPR","optional":false},{"name":"GitHub","optional":false},{"name":"GitHub Actions","optional":false},{"name":"GraphQL","optional":false},{"name":"GraphRAG","optional":false},{"name":"gRPC","optional":false},{"name":"HIPAA","optional":false},{"name":"JavaScript","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM","optional":false},{"name":"Looker","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"n8n","optional":false},{"name":"OpenAI","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Power BI","optional":false},{"name":"Prefect","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Pydantic AI","optional":false},{"name":"Python","optional":false},{"name":"RabbitMQ","optional":false},{"name":"RAG","optional":false},{"name":"Redis","optional":false},{"name":"Rest API","optional":false},{"name":"Rust","optional":false},{"name":"Semantic Search","optional":false},{"name":"SQL","optional":false},{"name":"Tableau","optional":false},{"name":"Terraform","optional":false},{"name":"TypeScript","optional":false},{"name":"WebAssembly","optional":false},{"name":"WebSockets","optional":false}],"status":"live","first_seen_at":"2026-10-20T11:35:00Z","employer_posted_date":null,"last_verified_at":"2026-10-20T11:35:00Z","board_verified":false,"closed_at":null,"days_open":5,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":5},"description":"We're seeking a Senior AI Integration Engineer with a strong engineering foundation to design and build the data and AI infrastructure that powers our automation and business intelligence initiatives.\n\nThis role sits at the intersection of data engineering, API development, and AI, focusing on creating robust data exchange systems and multi-agent AI workflows that replace legacy processes. You'll build systems where AI agents and humans collaborate through bidirectional work delegation—both for our internal operations and the client solutions we deliver. This is a senior-level position focused on technical leadership, where you will guide architectural decisions, review pull requests, and mentor junior engineers.\n\nThe candidate will have responsibilities across the following functions: \n\nTechnical Leadership and Mentorship: \nMentor junior engineers, providing constructive feedback and fostering technical growth.\nConduct thorough code reviews and pull request evaluations to maintain high code quality and best practices.\nLead the architecture and design of complex, interrelated AI and platform services.\nChampion robust design practices, including the application of design patterns and scalable and secure architecture principles.\n\nAI Agent Integration and Automation: \nIntegrate and orchestrate AI agents (LLMs, ML models, autonomous agents) into business workflows.\nDesign and implement multi-agent systems, agent-to-agent communication protocols, and data handoffs.\nBuild connectors and tools to enable AI agents to access enterprise data sources.\nDevelop feedback loops and observability systems for AI agent performance.\nCreate automated workflows that combine multiple AI capabilities (RAG systems, agent tools, multi-agent systems).\n\nAPI Development and Data Exchange: \nDesign, develop, and maintain scalable stateless APIs for agent communication and data exchange between systems and microservices.\nOptimise API performance, response times, token usage, and ensure high availability.\n\nData Infrastructure: \nMaintain and interact with data warehouses, data lakes, and vector databases.\nBuild ETL/ELT pipelines that prepare data for AI consumption.\nImplement data quality checks, validation, and governance frameworks.\nDesign data models that support both analytical queries and AI agent operations.\n\nBusiness Intelligence Support: \nCollaborate with BI teams to expose data through APIs for dashboards and reporting.\nBuild data APIs that serve real-time metrics and KPIs.\nCreate data aggregation services that support analytical workloads.\nEnable self-service data access through well-documented APIs and MCP servers.\n\nRequirements: \n5+ years of experience in software engineering, with a strong focus on backend systems, data engineering, or AI infrastructure.\nProven track record of leading technical projects, mentoring junior engineers, and driving architectural decisions in a collaborative environment.\n\nTechnical Expertise: \nAPI Development: Strong proficiency in Python and similar languages for API (REST, MCP) development.\nDatabase Expertise: Comfortable working with large data and pipelines. Strong knowledge of SQL and NoSQL databases (PostgreSQL, MongoDB, Redis, etc. )\nCloud Platforms: Experience with AWS, GCP, or Azure data and AI services.\nAPI Technologies: REST, GraphQL, gRPC, WebSockets, and message queues (Kafka, RabbitMQ, SQS, and SNS).\nExperience with AI agent frameworks (LangChain, LangGraph, LlamaIndex, Pydantic AI, AutoGen, CrewAI) and multi-agent systems.\nUnderstanding of LLM APIs (OpenAI, Anthropic, etc. ), prompt engineering, RAG systems, and vector databases, GraphRAG or Agentic RAG.\nFamiliarity with containerization and orchestration (Docker, Kubernetes).\nExperience with event-driven architectures and microservices.\nGeneral understanding of data security, encryption, and compliance requirements (GDPR, HIPAA, PHI, etc. )\nGeneral familiarity with DataOps, MLOps, or DevOps practices.\nPractical experience working with large datasets.\n\nLeadership and Soft Skills: \nStrong problem-solving abilities and architectural thinking.\nStrong mentorship and technical coaching capabilities.\nExcellent communication skills for cross-team collaboration.\nAbility to translate business requirements into technical solutions.\nSelf-motivated with the ability to work independently and in teams.\nPassion for emerging AI technologies and continuous learning.\n\nBonus Qualifications: \nContributions to open-source projects related to data or AI.\nUnderstanding of semantic search and embedding technologies.\nExperience with workflow orchestration tools (Airflow, Temporal, Prefect, n8n).\nPrior work with business intelligence tools (Tableau, Looker, Power BI).\n\nCategory Technologies: \nLanguages: Python, SQL, Rust, and Cypher.\nData Tools: Airflow.\nAPIs and Integration: FastAPI, GraphQL, Kafka, Redis, JavaScript/TypeScript, and WebAssembly (WASM).\nAI/ML: Multi-agent systems, Bedrock APIs, Pydantic AI, vector databases, RAG strategies, MCP.\nCloud: AWS (Lambda, S3, RDS, Bedrock, and ECS) or equivalent in GCP/Azure.\nInfrastructure: Docker, Kubernetes, Terraform, GitHub, and GitHub Actions.\nSkills\napi, backend, data, elt, etl, graphql, grpc, pipelines, python, rest","description_format":"text","description_chars":5211,"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":["Continuous learning"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":[],"lifecycle":[{"event":"open","at":"2026-10-20T11:35:00Z"}],"visa":[],"liveness":{"score":86,"band":"hot","label":"Hiring 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