AI Forward Deployed Engineer Training Course in Bhubaneswar (Live Online) by Uncodemy

• Role of a Forward Deployed Engineer in enterprise AI delivery
• How FDE differs from AI/ML engineering and solutions architecture
• Enterprise AI lifecycle: discovery to production
• Common enterprise AI use cases and failure modes
• Client-facing delivery models and engagement structures
• Overview of the modern AI stack: LLMs, RAG, agents, MCP

• Python fundamentals for AI engineering
• Working with libraries and virtual environments
• SQL for querying and joining enterprise datasets
• Git version control and collaborative workflows
• REST API design, request/response handling
• FastAPI basics and building API endpoints
• Authentication, error handling and API best practices

• How large language models work at a conceptual level
• Tokens, embeddings and context windows
• OpenAI APIs and Hugging Face model ecosystem
• Generative AI application patterns
• Model selection trade-offs for enterprise use
• Cost, latency and quality considerations

• Prompt design principles for reliable outputs
• System prompts, few-shot prompting and role prompting
• Context engineering for long and multi-turn interactions
• Structured output and function calling prompts
• Prompt evaluation and iteration
• Prompt security and injection risks

• Enterprise data sources: databases, documents, APIs
• Data ingestion and preprocessing pipelines
• Document parsing, chunking and metadata
• Embedding generation pipelines
• Data freshness and incremental updates
• Data governance and access control in AI pipelines

• What RAG is and why enterprises use it
• Vector databases and PostgreSQL/pgvector
• Retrieval strategies: dense, sparse and hybrid
• Reranking and relevance scoring
• Building an end-to-end RAG pipeline
• Evaluating RAG quality with RAGAS
• Common RAG failure modes and fixes

• Agent architectures and reasoning loops
• Tool calling and function schemas
• LangChain and LangGraph for agent orchestration
• LlamaIndex for knowledge agents
• MCP (Model Context Protocol) fundamentals
• Multi-step and multi-tool agent workflows
• Guardrails and failure handling for agents

• Building AI-enabled applications with FastAPI
• Integrating LLMs, RAG and agents into apps
• Backend services and async processing
• UI integration patterns for AI apps
• Managing state, sessions and conversation history
• Deploying AI app services to production

• Why AI evaluation differs from traditional software testing
• Evaluation metrics for LLM outputs (RAGAS, custom metrics)
• LangSmith and Langfuse for tracing and observability
• Monitoring production AI systems
• Handling drift, regressions and failures
• Continuous evaluation and feedback loops

• Threat models for enterprise AI systems
• Prompt injection and data exfiltration risks
• Output moderation and safety filters
• Data privacy and PII handling in AI pipelines
• Access control and tenant isolation
• Compliance considerations for enterprise deployments

• Containerising AI services with Docker
• AWS and Azure fundamentals for AI deployment
• CI/CD pipelines for AI applications
• Secrets and environment configuration
• Scaling and performance tuning
• Rollback strategies and production readiness

• Running client discovery and scoping sessions
• Translating business problems into AI solution designs
• Solution architecture for RAG and agent systems
• Estimating effort, cost and risk
• Presenting AI solutions to enterprise stakeholders
• Consulting communication and documentation

• End-to-end enterprise AI deployment project
• RAG pipeline + AI agent integration in a client-style scenario
• Deployment on cloud with CI/CD
• Evaluation, observability and guardrails applied to the capstone
• Capstone presentation and defence
• Portfolio-ready deliverables

Bhubaneswar's enterprise software, IT services and fintech clusters have quickened their uptake of applied AI, generating genuine demand for engineers capable of taking AI from proof-of-concept to production within client organisations. The city's blend of large IT-services firms, product companies, BFSI technology teams and consulting practices means Forward Deployed Engineer skills find application across sectors — not just in research labs.

Interview panels in Bhubaneswar typically evaluate three aspects. First, whether you can design and defend a RAG or agent system architecture suited to a business problem. Second, whether you possess genuine deployment experience — Docker, CI/CD, cloud, monitoring, evaluation. Third, whether you can sustain a client-facing conversation: scoping, translating business requirements, presenting solutions. Candidates who can guide an interviewer through a completed enterprise AI deployment, rather than merely a model notebook, stand out the quickest.

This program is structured to develop precisely those three capabilities via live sessions, hands-on projects, cloud deployment work and a capstone that replicates real client engagements.

Since the program covers enterprise AI fundamentals, Python/SQL/Git/API engineering, LLM applications, prompt and context engineering, RAG, agents and MCP, evaluation, security, cloud deployment and customer discovery, graduates can pursue several client-facing AI delivery roles rather than a single title. The table below associates each role with the core tools most relevant to it.

Over the course of 2–5 years, professionals typically advance from entry-level Forward Deployed Engineer or AI Solutions Engineer roles to senior FDE positions and, ultimately, AI Solutions Architect roles. This progression involves managing more complex client projects, taking greater ownership of system architecture, and developing leadership skills. The program's core competencies — RAG, AI agents, MCP, evaluation, deployment, and customer discovery — provide a strong foundation for long-term career growth in this field.

Compensation for Forward Deployed Engineer and closely related AI delivery roles in Bhubaneswar differs by employer, experience and technical scope. Entry-level AI Forward Deployed Engineers usually begin at ₹6–10 LPA, while senior FDEs and AI Solutions Architects can earn considerably more. The ranges below are indicative market ranges Uncodemy applies across its course pages.

Recent salary reports in India indicate that Forward Deployed Engineers (FDEs) typically earn between ₹9 LPA and ₹27 LPA, depending on their experience, employer, and technical expertise (Source: Express Computer).

The salary estimates are based on information from AmbitionBox, Glassdoor, Indeed, Express Computer, and Uncodemy's placement records, reviewed in October 2026. Publicly available salary data is compared with internal placement information, and the estimates are updated periodically to reflect general market trends. Actual salary offers in Bhubaneswar may differ depending on the employer's size, industry, candidate performance during interviews, previous experience, and job responsibilities. Therefore, these figures should be considered indicative estimates for career planning rather than guaranteed salary outcomes.

Demand for Forward Deployed Engineer skills in Bhubaneswar extends across multiple industries, and Uncodemy's 850+ hiring partners reach into many of them. The sectors below are where Bhubaneswar-based AI deployment skills are most frequently applied today:

• Enterprise software & SaaS: Product companies building AI features and needing engineers who can deliver them to enterprise clients.
• IT services: Large services firms deploying RAG and agent solutions for their global client base.
• Fintech: Payment platforms, lending and wealth-tech companies applying AI agents and RAG in customer-facing and internal workflows.
• Product engineering: Captive centres and GCCs designing AI-enabled products that must run in production at scale.
• Consulting & system integrators: Advisory and SI firms delivering enterprise AI programmes for large clients.

As every sector relies on the same foundation — RAG, agents, evaluation, deployment and client-facing delivery — a broad Forward Deployed Engineer program enables you to apply across industries instead of committing to one early in your career.

Becoming an AI Forward Deployed Engineer involves a series of skills, each building on the last. The seven steps below follow this program's order, so you always understand what comes next and why it matters.

1. FDE & Enterprise AI Fundamentals: Understand what Forward Deployed Engineers do, how enterprise AI delivery works, and where the role fits.
2. Python, SQL, Git & API Engineering: Build the engineering foundation — Python, SQL, Git workflows and REST API development with FastAPI.
3. LLM Fundamentals & Generative AI: Learn how LLMs work, how to use OpenAI APIs and Hugging Face models, and how to design generative AI applications.
4. Prompt & Context Engineering: Master prompt design, structured output, function calling and context handling for reliable outputs.
5. RAG & Enterprise Knowledge Systems: Build end-to-end RAG pipelines with vector databases, retrieval, reranking and RAGAS evaluation.
6. AI Agents, Tool Calling & MCP: Design agent architectures, tool calling schemas, LangChain and LangGraph workflows, and MCP integrations.
7. Cloud Deployment, Evaluation & Capstone: Deploy AI systems on cloud with Docker and CI/CD, add evaluation and observability, and complete the enterprise AI capstone.

Timeline: [CONFIRM — duration pending, see flag above before writing a specific month count here].

When selecting an AI course, consider the key elements that can influence your learning experience and career prospects, including course duration, total training hours, live instruction, tools covered, hands-on project experience, and career assistance. The table below highlights these aspects of the program, making it easier to evaluate its features against other available courses.

Compared with Uncodemy's LLM Engineering and RAG Engineering courses, the AI Forward Deployed Engineer program provides broader training in enterprise AI implementation, covering customer discovery, RAG, AI agents, MCP, evaluation, deployment, and consulting. While the LLM Engineering and RAG Engineering courses focus on developing specialised technical expertise in their respective areas, this program is designed for those seeking careers in client-facing enterprise AI delivery. Learners who already have a strong understanding of LLMs or RAG and want to specialise further may prefer a focused course. The capstone project and client-scenario interview preparation also add practical value, distinguishing this program from more specialised alternatives.

Uncodemy does not operate a classroom in Bhubaneswar, so learners from the city attend this AI Forward Deployed Engineer program entirely live online. That does not make it a watered-down version of the course. You join the same batches, learn from the same trainers and follow the same 13-module curriculum as learners in Uncodemy's classroom programs. Sessions take place in real time, so you can ask questions, share your screen for code review and discuss RAG or agent architecture with the trainer while class is in progress.

Studying online also eliminates the daily commute across the city, which matters a great deal to working professionals. You can choose a weekday or weekend batch around your job and revise using session recordings.

If you would rather attend a physical classroom and can travel, in-person training remains available at Uncodemy's Delhi and Noida centres. The fee and curriculum are identical in both modes.

The course includes 177 learning hours delivered across 13 modules, with more than 100 live sessions to support structured, practical learning.

No prior coding experience is required. However, a basic understanding of computers and a willingness to learn technical concepts can help you get started.

The curriculum covers 20+ tools and technologies, including Python, SQL, Git, GitHub, REST APIs, FastAPI, OpenAI APIs, Hugging Face, LangChain, LangGraph, LlamaIndex, MCP, RAG, vector databases, PostgreSQL/pgvector, Docker, AWS/Azure, CI/CD, LangSmith/Langfuse and RAGAS.

Yes. The course includes a 15-hour module on RAG and Enterprise Knowledge Systems, followed by a 15-hour module on AI Agents, Tool Calling and MCP. Both modules include hands-on learning and project work.

The capstone is covered in Module 13, Enterprise AI Deployment & Capstone Project, which runs for 25 hours. You build and present an end-to-end enterprise AI solution featuring a RAG pipeline, an AI agent, cloud deployment with CI/CD, and evaluation and guardrails.

The program is delivered through more than 100 live sessions. Session recordings are also available for revision, so you can review topics later or catch up if you miss a class.

The course fee is ₹19,500 plus 18% GST, bringing the total to ₹23,010. The same fee applies to live online learning from Bhubaneswar and classroom training in Delhi or Noida, with no additional location-based charge stated.

Yes. An indicative EMI option is ₹3,835 per month over 6 months, which totals ₹23,010. The final instalment amount, tenure and eligibility may vary depending on the payment or financing option available to you.

You can enquire about a demo through the course enquiry form or by contacting the counselling team. Demo availability depends on the current batch schedule, and the team can share the available options with you.

Yes. The program includes career and placement assistance such as resume and LinkedIn guidance, mock interviews, and access to openings through a network of 850+ hiring partners. This support can help with your job search, but employment is not guaranteed.

The course is designed to help you prepare for entry-level AI Forward Deployed Engineer opportunities, with an indicative salary range of ₹6–10 LPA. Actual outcomes depend on your skills, projects, interview performance and market conditions; no specific role or salary is guaranteed.

Indicative salary ranges listed for this career path begin at ₹6–10 LPA for entry-level FDE roles and may reach ₹20–35 LPA for AI Solutions Architect positions. Express Computer has reported FDE salaries in India broadly around ₹9–27 LPA. These are market estimates, not guaranteed offers.

Depending on your skills and project experience, you may pursue roles such as Forward Deployed Engineer, AI Solutions Engineer, Applied AI Engineer, AI Integration Engineer or Entry-Level Forward Deployed Engineer. Senior FDE positions generally require relevant professional experience, and the best-fit path will depend on your strengths and portfolio.

The course is suitable for software and IT professionals, freshers with programming fundamentals, data or machine learning engineers, and people moving from technical consulting into AI delivery. You will need a laptop and a reliable internet connection to participate in the learning activities.

No. Salary figures are indicative estimates, placement assistance does not guarantee employment, and review counts are based on public platform information that can change over time. These details are provided as guidance to help you assess the program.

No. The program is offered live online for learners in Bhubaneswar. The classroom locations listed for in-person training are in Delhi (Laxmi Nagar/Shakarpur) and Noida (Sector 1), so attending in person would require travelling to one of those centres.

This program takes a broader, client-facing approach to enterprise AI delivery, covering customer discovery, RAG, agents, MCP, evaluation and deployment. LLM Engineering and RAG Engineering focus more deeply on their specific technical areas, making them alternatives for learners who want to specialise in one domain.