Forward Deployed Engineer (FDE) — AI & Enterprise Solutions

Job Description: Forward Deployed Engineer (FDE) — AI & Enterprise Solutions
Company: XLAIRS ServicesAI
Position: Forward Deployed Engineer (FDE)
Location: India / USA / Remote, with customer-site deployment as required
Experience: 4–8+ years of relevant software engineering experience
Employment: Full-time / Contract / Customer Deployment
About the Role
ServicesAI is seeking highly capable Forward Deployed Engineers to design, develop, integrate, and deploy production-grade AI and enterprise software solutions directly within customer environments.
The ideal candidate combines strong software engineering skills, hands-on AI/LLM implementation experience, cloud and data engineering capabilities, and the ability to collaborate directly with enterprise customers.
FDEs will work at the intersection of engineering, AI, enterprise architecture, and customer delivery, translating complex business challenges into scalable, secure, measurable solutions.
Key Responsibilities
1. Customer Engagement and Solution Discovery
Work directly with enterprise customers to understand business workflows, technical requirements, and operational challenges.
Translate ambiguous business problems into technical specifications, solution architectures, and implementation plans.
Collaborate with customer engineering, product, data, security, and business teams.
Lead technical workshops, solution demonstrations, and deployment discussions.
2. Software Engineering and Application Development
Design, build, test, and deploy production-grade applications using Python, Java, JavaScript/TypeScript, or comparable languages.
Implement automated testing, debugging, performance optimization, and production troubleshooting.
Work with relational and NoSQL databases.
3. AI, LLM, and Agentic Solution Development
Develop applications leveraging LLMs and generative AI models.
Build AI agents with tool calling, workflow orchestration, and external system integration.
Integrate AI services using model APIs and enterprise AI platforms.
Develop evaluation frameworks to measure model accuracy, reliability, latency, and task success.
Implement guardrails, human-in-the-loop controls, and observability for AI applications.
4. Cloud Engineering and Enterprise Integration
Deploy applications on AWS, Azure, or Google Cloud.
Work with Docker, Kubernetes, CI/CD pipelines, and infrastructure automation.
Integrate AI applications with enterprise databases, APIs, SaaS platforms, and existing business systems.
Design for security, scalability, availability, and operational reliability.
Monitor production systems and resolve deployment and integration issues.
5. End-to-End Technical Delivery
Own technical delivery from discovery and prototyping through production deployment and handover.
Develop proof-of-concepts and convert successful prototypes into production systems.
Manage technical dependencies, troubleshoot blockers, and make engineering trade-offs.
Document architecture, deployment procedures, and operational requirements.
Support adoption, performance measurement, and continuous improvement.
Required Qualifications
Bachelor's or master's degree in Computer Science, Engineering, Information Technology, or equivalent practical experience.
4+ years of professional software engineering experience.
Strong hands-on programming skills in Python and at least one additional language such as Java, JavaScript, TypeScript, or Go.
Experience building and deploying production applications.
Experience designing REST APIs, backend services, and database integrations.
Working knowledge of SQL, data structures, and software architecture.
Experience with at least one major cloud platform: AWS, Azure, or GCP.
Familiarity with Docker, Git, CI/CD, automated testing, and production debugging.
Practical experience integrating LLM APIs or developing AI-powered applications.
Strong analytical problem-solving and communication skills.
Ability to work independently with customers in ambiguous, fast-changing environments.
Preferred Qualifications
Production experience with AI agents, RAG, LangGraph, LlamaIndex, or comparable frameworks.
Experience with OpenAI, Anthropic Claude, Gemini, or open-source LLMs.
Knowledge of MCP, tool calling, agent orchestration, and agent evaluation.
Experience with vector databases such as Pinecone, Weaviate, Milvus, or pgvector.
Experience with Databricks, Snowflake, Spark, or enterprise data platforms.
Experience with Kubernetes, Terraform, and cloud-native architectures.
Familiarity with model evaluations, LLM observability, security controls, and responsible AI deployment.
Prior experience in consulting, solutions engineering, technical implementation, or customer-facing engineering.
Experience in regulated enterprise environments such as financial services, healthcare, insurance, or cybersecurity.
Key Success Measures
Successful deployment of production-ready solutions.
Time from technical discovery to working prototype and production release.
Solution reliability, performance, security, and quality.
Customer adoption and measurable business impact.
Ability to resolve complex engineering and integration challenges.
Reusability of delivered engineering assets.

Work arrangement
Yes