Infosys
Website:
infosys.com
Job details:
About the Company
We are a new-generation Agentic AI practice driven by a mission to reimagine industries, amplify enterprise processes, solve complex problems, and build sentient intelligent enterprises. We combine the strategic depth of consulting with the technical excellence of AI engineering to help organizations evolve from traditional workflows to agentic enterprises — intelligent, self-learning, and autonomous. We build and deploy AI agents that transform how businesses think, decide, and act.
About the Role
The Forward Deployment Engineer is a hybrid AI engineer, systems integrator, and customer-facing solution architect responsible for embedding Agentic AI solutions within enterprise environments. You will bridge AI solution engineering and real-world enterprise deployment, working directly with customer teams to design, implement, integrate, and operationalize agentic AI systems across business processes. This is not a lab role. You operate at the intersection of AI engineering, enterprise systems integration, process transformation, and client consulting — converting platform capabilities into real-world solutions that accelerate enterprise AI adoption.
Responsibilities
- Identify (~10%) — Use Case Discovery & Solution Design
- Engage with client stakeholders to identify high-value Agentic AI use cases
- Translate business workflows into AI-enabled automation opportunities
- Conduct technical discovery workshops; map current processes and AI intervention points
- Design agent workflows, orchestration patterns, and API integration architectures
- Build pilot agentic solutions with solution wireframes and architecture blueprints
- Commit (~0–10%) — Technical Validation & Deal Support
- Provide technical credibility during pre-sales; conduct proof-of-concept demonstrations
- Validate integration feasibility, security & compliance, and infrastructure readiness
- Estimate deployment complexity and effort through architecture reviews
- Deploy (~70–80%) — Embedded Implementation (Core Responsibility)
- Technical Implementation: Develop agentic workflows, build multi-agent orchestration, integrate enterprise APIs (CRM, ERP, data warehouses, workflow systems, SaaS), build data pipelines, deploy LLM & RAG services
- Workflow Engineering: Decompose business processes into AI-orchestrated workflows — agent role definition, task orchestration, tool invocation, decision logic design
- Infrastructure & Deployment: Deploy on cloud/enterprise infrastructure, configure vector databases, implement monitoring & observability, ensure scalability and reliability
- Solution Customization: Create custom microservices, APIs, agent orchestration logic, and workflow automation systems
- Customer Collaboration: Co-design technical workflows, run stakeholder workshops, iterate through rapid deployment cycles, embed directly with client engineering teams
- Consume (~10–20%) — Adoption & Optimization
- Train end users, deliver workshops, provide usage playbooks, support change management
- Monitor system performance, troubleshoot issues, optimize agent workflows
- Serve as voice of the customer — identify product gaps, propose feature improvements, influence platform roadmap
Qualifications
- Education: Bachelor's or Master's in Computer Science, Engineering, AI/ML, or related technical discipline, MBA Preferred.
- Certifications in AI/ML, Cloud AI (Azure/AWS/GCP), or MLOps preferred.
Required Skills
- Experience: 5–12 years in software engineering, AI engineering, data engineering, or systems integration
- AI / Machine Learning: Strong knowledge of LLMs, RAG, prompt engineering, agent architectures, multi-agent orchestration, tool use & function calling, model evaluation
- Frameworks: LangChain, LangGraph, Semantic Kernel, CrewAI, AutoGen, OpenAI / Anthropic APIs
- Software Engineering: Strong programming: Python, TypeScript (Java/Go optional), API development, microservices architecture, distributed systems, containerization, Docker, Kubernetes, REST APIs, gRPC, event-driven systems
- Data Engineering: Data pipelines, ETL workflows, vector databases, embedding systems, Tools: Pinecone, Weaviate, Milvus, FAISS, Elasticsearch
- AI Infrastructure: Cloud AI deployment, GPU workloads, model inference services, Platforms: AWS, Azure, GCP — model serving, distributed inference, orchestration frameworks
- Systems Integration & Enterprise Architecture: Integration with enterprise software, legacy systems, SaaS applications, API orchestration, workflow automation, middleware integration, Understanding of enterprise IT architectures, data governance, security frameworks, compliance, identity management
- Process Transformation: Business workflow mapping, automation opportunity identification, process reengineering, Design thinking and process reimagination, user experience design, Ability to map: Business Process → AI Workflow → Agent System
- Client Engagement: Stakeholder communication, workshop facilitation, requirements discovery, solution storytelling, Fluency in translating between technical teams and business stakeholders
Preferred Skills
- Ideal background: Candidates often come from: AI startups, consulting firms, developer platforms, enterprise SaaS companies, cloud providers
- Previous roles: AI Engineer, ML Engineer, Solutions Architect, Full Stack Engineer, Platform Engineer
Pay range and compensation package
Where we're hiring: 📍 Bengaluru · Chennai · Hyderabad — hybrid collaboration with our global AI hubs. FDEs operate close to clients — embedding directly into their environment to design, build, and deploy real-world AI systems. Occasional travel may be required for on-site deployments and co-creation sessions
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Equal Opportunity Statement
Join us if you're an engineer who doesn't just write code — but builds intelligence. Be at the frontier where AI meets business, helping organizations evolve into agentic, self-learning enterprises
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Click on Apply to know more.