AI Forward Deployed Engineer Training Course in Bilaspur (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

Applied AI adoption has accelerated across Bikaner's enterprise software, IT services and fintech clusters, creating genuine demand for professionals able to carry AI from proof-of-concept through to production inside client organisations. The city's blend of large IT-services houses, product firms, BFSI technology teams and consulting practices means Forward Deployed Engineer capabilities find application across a wide range of industries — not solely inside research environments.

Recruiters and interview panels across Bikaner typically scrutinise three dimensions. First, whether you can design and justify a RAG or agent system architecture aligned with a business problem. Second, whether you carry credible deployment experience — Docker, CI/CD, cloud, monitoring and evaluation. Third, whether you can sustain a client-facing discussion: scoping, translating business needs, presenting solutions. Candidates who can walk an interviewer through a completed enterprise AI deployment — rather than merely a model notebook — distinguish themselves the fastest.

This course is purpose-built to develop those exact three competencies through live sessions, hands-on projects, cloud deployment exercises and a capstone that mirrors real client engagements.

Because the syllabus spans 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 target multiple client-facing AI delivery roles rather than a single job title. The table below maps each role to the core tools most relevant to it.

Over a typical 2–5 year horizon, professionals advance from entry-level Forward Deployed Engineer or AI Solutions Engineer positions to senior FDE roles and, eventually, AI Solutions Architect positions. That progression involves handling more complex client engagements, taking greater ownership of system architecture and building leadership capabilities. The program's core competencies — RAG, AI agents, MCP, evaluation, deployment and customer discovery — lay a strong foundation for sustained career growth in this domain.

Remuneration for Forward Deployed Engineer and closely related AI delivery roles in Bikaner varies with employer, experience and technical scope. Entry-level AI Forward Deployed Engineers typically start at ₹6–10 LPA, while senior FDEs and AI Solutions Architects can command significantly more. The figures below represent indicative market ranges Uncodemy applies across its course pages.

Recent Indian salary reports suggest Forward Deployed Engineers (FDEs) generally earn between ₹9 LPA and ₹27 LPA, depending on experience, employer and technical expertise (Source: Express Computer).

These salary estimates draw on information from AmbitionBox, Glassdoor, Indeed, Express Computer and Uncodemy's placement records, reviewed in October 2026. Publicly available salary data is weighed against internal placement information, with estimates refreshed periodically to reflect general market trends. Actual offers in Bikaner may differ based on employer size, industry, candidate interview performance, prior experience and job responsibilities. These figures should therefore be treated as indicative planning estimates rather than guaranteed salary outcomes.

Forward Deployed Engineer demand in Bikaner spans several industries, and Uncodemy's 850+ hiring partners reach into many of them. The sectors listed below are where Bikaner-based AI deployment skills are applied most frequently 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.

Because every sector rests on the same foundation — RAG, agents, evaluation, deployment and client-facing delivery — a broad Forward Deployed Engineer program lets you apply your skills across industries rather than committing to one too early in your career.

Becoming an AI Forward Deployed Engineer is a journey of layered skills, each building on the last. The seven steps below mirror this program's sequence, so you always know 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 evaluating an AI course, weigh the factors that shape your learning experience and career prospects — course duration, total training hours, live instruction, tools covered, hands-on project work, and career assistance. The table below summarises these aspects of the program, making it easier to compare against other available courses.

Relative to Uncodemy's LLM Engineering and RAG Engineering courses, the AI Forward Deployed Engineer program offers broader training in enterprise AI implementation — spanning customer discovery, RAG, AI agents, MCP, evaluation, deployment and consulting. Whereas the LLM Engineering and RAG Engineering courses dig deeper into specialised technical areas, this program is built for people pursuing client-facing enterprise AI delivery careers. Learners who already possess strong LLM or RAG knowledge and wish to specialise further may prefer one of the focused courses. The capstone project and client-scenario interview preparation add practical value that distinguishes this program from more specialised alternatives.

Because Uncodemy does not run a physical classroom in Bikaner, candidates from the city attend this AI Forward Deployed Engineer program fully live online. That does not make it a diluted version of the course. You join the same batches, learn from the same trainers and follow the identical 13-module curriculum as learners in Uncodemy's classroom programs. Sessions run 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.

Learning online also removes the daily commute across the city — a meaningful advantage for working professionals. You can pick a weekday or weekend batch around your job and revisit session recordings for revision.

Should you prefer a physical classroom and are able to travel, in-person training remains available at Uncodemy's Delhi and Noida centres. The fee and curriculum stay identical in both modes.

The course spans approximately 5-6 months and 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 Bilaspur 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 ₹5–9 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 ₹5–9 LPA for entry-level FDE roles and may reach ₹18–32 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 Bilaspur. 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.