{"id":1909136,"url":"https://alion.io/job/hcltech-solution-architect-dfs-3","title":"Solution Architect (DFS)","company":{"id":225,"name":"HCLTech","domain":"hcltech.com","url":"https://alion.io/company/hcltech","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"SuccessFactors","truth_index":null},"role":"Solutions","role_family":"Solutions","seniority":"staff","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":[],"countries":[],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AgentOps","optional":false},{"name":"AI Agents","optional":false},{"name":"Amazon CloudWatch","optional":false},{"name":"Amazon ECS","optional":false},{"name":"Amazon EKS","optional":false},{"name":"Amazon EventBridge","optional":false},{"name":"Amazon S3","optional":false},{"name":"API Gateway","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"AWS Bedrock AgentCore","optional":false},{"name":"AWS Lambda","optional":false},{"name":"AWS Step Functions","optional":false},{"name":"AWS Strands Agents","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"Function Calling","optional":false},{"name":"Git","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"IAM","optional":false},{"name":"Incident Management","optional":false},{"name":"ITSM","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Rest API","optional":false},{"name":"Tool Use","optional":false},{"name":"A2A","optional":true},{"name":"AIOps","optional":true},{"name":"Kubernetes","optional":true},{"name":"OpenTelemetry","optional":true},{"name":"ServiceNow","optional":true},{"name":"Terraform","optional":true}],"status":"live","first_seen_at":"2026-10-05T13:59:16Z","employer_posted_date":"2026-10-05","last_verified_at":"2026-10-11T04:53:01Z","board_verified":true,"closed_at":null,"days_open":5,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":5},"description":"Job Summary\nSenior Forward Deployment Engineer - AWS Agentic AI\n\nJob Type\n\nFull-Time\n\nExperience\n\n8-12 years overall; at least 3 years in Generative AI / Agentic AI with significant enterprise production delivery on AWS\n\nLocations\n\nNoida, Hyderabad, Chennai, Bangalore, Pune\n\nPrimary Focus\n\nSolution architecture, customer technical leadership, production strategy, technical governance and FDE leadership\n\nRole Overview\nWe are looking for a Senior Forward Deployment Engineer (FDE) to lead the technical architecture and end-to-end deployment of enterprise Generative AI and Agentic AI solutions for strategic customers on AWS. The Senior FDE is a customer-facing solution architect who can code: they own the technical success of the customer engagement, make architecture and deployment decisions, lead customer workshops, review implementation, drive production readiness, and mentor FDEs.\nCustomer-facing solution architect who can code\nOwns technical success of the customer engagement\nMakes architecture and production decisions\nLeads FDEs and influences the platform roadmap\n\nTypical time allocation\n~50% architecture and technical leadership\n~30% customer leadership\n~20% engineering oversight and mentoring\nKey Responsibilities\nOwn the overall technical delivery of enterprise Agentic AI deployments from discovery and architecture through production rollout, hypercare, and transition to BAU.\nLead customer discovery, technical workshops, architecture reviews, executive technical discussions, solution demonstrations, POCs, pilots, and production planning.\nDesign customer-specific AI and AWS architectures using Amazon Bedrock, Amazon Bedrock AgentCore, AWS services, enterprise data sources, applications, security controls, and operational services.\nDefine agent architecture including multi-agent patterns, Planner/Critic/Supervisor/Orchestrator designs, domain agents, Skills, tools, RAG, human-in-the-loop controls, state, and enterprise integrations.\nDefine the integration architecture for REST APIs, MCP tools, databases, ITSM platforms, monitoring systems, identity systems, messaging systems, and proprietary applications.\nDecide which requirements should be implemented through configuration, customer-specific extensions, or reusable platform capabilities; drive the appropriate engineering path.\nDefine production deployment architecture across Amazon Bedrock AgentCore Runtime, Gateway, Memory, Identity, Policy and Evaluations, Lambda, ECS, EKS, API Gateway, EventBridge, Step Functions, S3, CloudWatch, IAM, networking, and other appropriate AWS services.\nDefine production architecture requirements for availability, scalability, security, resiliency, observability, release management, rollback, disaster recovery, and operational support.\nLead security and identity architecture discussions covering IAM, role-based access, authentication, authorization, Secrets Manager, data protection, auditability, VPC controls, and network security.\nDefine and review production readiness criteria covering functionality, security, performance, reliability, evaluation, observability, governance, supportability, and operational readiness.\nDefine the customer AgentOps strategy including telemetry, tracing, logging, metrics, evaluation, quality monitoring, cost monitoring, and operational dashboards.\nLead complex troubleshooting and root-cause analysis for production issues involving agents, models, RAG, tools, integrations, networking, authentication, and AWS infrastructure.\nDrive reliability, latency, scalability, model performance, and AI cost optimization across customer deployments.\nDefine and review CI/CD, release management, environment promotion, versioning, rollback, and deployment processes.\nReview architecture, integration code, deployment plans, and technical deliverables produced by FDEs; establish engineering quality standards.\nMentor and technically guide FDEs and Associate FDEs working on customer deployments.\nEstablish reusable deployment patterns, integration patterns, reference architectures, runbooks, onboarding standards, and troubleshooting playbooks.\nIdentify recurring customer requirements and drive their conversion into reusable agents, Skills, tools, connectors, frameworks, and platform capabilities with Agent Development / Platform Engineering teams.\nAct as the senior technical escalation point for complex customer issues and major production incidents.\nProvide technical feedback to Product, Agent Development, Platform Engineering, Security, Cloud Architecture, and Operations teams and influence platform roadmap priorities.\nLead technical handover, customer enablement, operational readiness, and transition to support/BAU teams.\nSkill Requirements\nMust Have Skills\nStrong Python and software engineering skills with the ability to review and guide production code.\nExtensive hands-on experience with Generative AI, LLMs, RAG, prompt engineering, tool calling, evaluation, and multi-agent architectures.\nStrong hands-on experience with Amazon Bedrock and/or Amazon Bedrock AgentCore, with LangGraph/LangChain/Strands Agents or equivalent frameworks in production.\nStrong AWS architecture and engineering experience across Amazon Bedrock and core AWS services.\nStrong enterprise integration experience across REST APIs, databases, ITSM platforms, monitoring systems, identity platforms, and customer-specific applications.\nStrong understanding of AWS IAM, role-based access, OAuth, secrets management, authentication, authorization, security, and enterprise networking.\nStrong experience with containerized applications, Docker, ECS/EKS, Lambda, AgentCore Runtime, and production deployment patterns.\nStrong experience with CI/CD, Git, release management, versioning, and production operations.\nStrong experience with observability, tracing, logging, monitoring, debugging, evaluation, and production incident management.\nStrong understanding of RAG architecture, knowledge integration, retrieval, evaluation, and agent quality measurement.\nExperience with Responsible AI, guardrails, AI security, data protection, and enterprise governance.\nStrong customer-facing communication skills and ability to lead architecture conversations with senior technical stakeholders.\nPreferred Skills\nDeep experience with Amazon Bedrock, AgentCore Runtime, Gateway, Memory, Identity, Policy, Evaluations, and AgentCore Observability.\nExperience with OpenTelemetry/ADOT, enterprise AgentOps, and agent evaluation practices.\nExperience with MCP, A2A, AWS SDKs, and enterprise agent interoperability.\nExperience with Terraform / Infrastructure-as-Code and enterprise AWS landing zones.\nStrong experience with Lambda, ECS, EKS, API Gateway, EventBridge, Step Functions, SQS/SNS, S3, Secrets Manager, CloudWatch, networking, VPC endpoints, and private connectivity.\nExperience with ServiceNow, ITSM, CloudOps, SRE, AIOps, infrastructure automation, or enterprise operations.\nExperience designing highly available, scalable, secure production architectures.\nExperience leading POCs, pilots, MVPs, production rollouts, and strategic enterprise deployments.\nExposure to multiple cloud platforms is advantageous.\nOther Requirements\nQualifications\nBachelor’s or Master’s degree in Computer Science, Engineering, Information Technology, or related field.\n8-12 years of overall software engineering, cloud engineering, solution architecture, or equivalent technical experience.\nAt least 3 years of practical Generative AI / Agentic AI experience.\nDemonstrated track record of delivering multiple enterprise technology or AI solutions into production.\nDemonstrated customer-facing technical leadership and architecture ownership.\nKey Attributes\nStrong customer-facing presence and ability to build technical trust with enterprise customers.\nStrong ownership of outcomes rather than individual tasks.\nExcellent architecture and problem-solving skills.\nComfortable operating across AI engineering, AWS cloud architecture, integration, security, and operations.\nAble to make pragmatic decisions between reusable platform capabilities and customer-specific implementation.\nStrong mentoring and technical leadership 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