AI Forward Deployed Engineer Training Course in Sikar (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

Sikar's professional landscape has traditionally centred on education, agriculture and small-scale manufacturing, and its technical talent increasingly looks to Jaipur's IT corridor, NCR employers and remote-first companies for applied AI roles. As enterprise AI adoption spreads across Rajasthan's IT-services, product-engineering and fintech firms, demand is growing for engineers who can take AI from a proof-of-concept to a production system inside a client organisation. The city's strong education ecosystem and growing pool of engineering graduates make it a realistic base for candidates targeting Forward Deployed Engineer careers in nearby metros or through remote positions.

Interview panels usually assess three things first. One, whether you can design and defend a RAG or agent system architecture that suits a business problem. Two, whether you have genuine deployment experience — Docker, CI/CD, cloud, monitoring, evaluation. Three, whether you can handle a client-facing conversation: scoping, translating business requirements, presenting solutions. Candidates who can walk an interviewer through a completed enterprise AI deployment, rather than just a model notebook, stand out fastest.

This program is built to develop exactly those three capabilities through live sessions, hands-on projects, cloud deployment work and a capstone that mirrors real client engagements.

Because the program 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 several client-facing AI delivery roles rather than a single title. The table below maps each role to the core tools most relevant to it.

Across a 2–5 year progression, professionals typically advance from entry-level Forward Deployed Engineer or AI Solutions Engineer positions into senior FDE roles and eventually AI Solutions Architect roles, assuming larger client engagements, deeper architecture ownership and team leadership. The capabilities developed in this program — RAG, agents, MCP, evaluation, deployment and customer discovery — lay the groundwork for that trajectory.

Pay for Forward Deployed Engineer and adjacent AI delivery roles in Sikar and the surrounding region varies by employer, experience and technical scope. Entry-level AI Forward Deployed Engineers usually start at ₹6–10 LPA, while senior FDEs and AI Solutions Architects can earn considerably more. The ranges below are indicative market ranges Uncodemy uses across its course pages.

Current India reporting places Forward Deployed Engineer (FDE) salaries broadly around ₹9–27 LPA, although actual compensation varies substantially by experience, company and technical scope (source: Express Computer).

Sources reviewed: AmbitionBox, Glassdoor, Indeed, Express Computer and Uncodemy placement records. Reviewed: October 2026. These ranges are compiled by comparing publicly reported salary data against internal placement information, and they are reviewed periodically so they stay broadly aligned with the market. Actual offers in Sikar and nearby markets depend on employer size and sector, interview performance, prior experience and the specific responsibilities of the role, so treat the table as a planning guide.

Demand for Forward Deployed Engineer skills reaches Sikar learners through several industries, and Uncodemy's 850+ hiring partners span many of them. The sectors below are where AI deployment skills are most commonly applied for professionals based in and around Sikar:

• Enterprise software & SaaS: Product companies adding AI capabilities 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, including teams across Jaipur and NCR.
• 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 across sectors.

Since all these sectors rest on a shared foundation — RAG, agents, evaluation, deployment and client-facing delivery — a broad Forward Deployed Engineer program prepares you to work across industries instead of locking into one early in your career.

Becoming an AI Forward Deployed Engineer follows a sequence of skills, each building on the one before. The seven steps below mirror this program's order, so you always know what comes next and why it matters.

1. FDE & Enterprise AI Fundamentals: Learn what Forward Deployed Engineers do, how enterprise AI delivery is structured, and where the role fits.
2. Python, SQL, Git & API Engineering: Build the engineering base — Python, SQL, Git workflows and REST API development with FastAPI.
3. LLM Fundamentals & Generative AI: Understand 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 results.
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: 5-6 months, covering all seven steps from fundamentals through the capstone.

Before selecting an AI course, compare the factors that shape your outcome: duration, total learning hours, live teaching, tool coverage, project depth and career support. The table below lays out these factors for this program so you can weigh them against any alternative.

Unlike Uncodemy's LLM Engineering or RAG Engineering courses, this AI Forward Deployed Engineer program is the broader, client-facing enterprise-deployment track — covering discovery, RAG, agents, MCP, evaluation, deployment and consulting. LLM Engineering and RAG Engineering are narrower, deeper-technical specialisations focused on their respective domains. If your goal is a client-facing enterprise AI delivery career, this program fits better. If you already have strong LLM or RAG depth and want to go further in one specific area, a narrower specialisation may suit you more. The capstone project and client-scenario interview preparation further separate this program from narrower alternatives.

Because Uncodemy has no classroom facility in Sikar, participants from the city attend this AI Forward Deployed Engineer program entirely live online. It is not a lighter version of the course in any way. You join the same batches, learn from the same trainers and cover the same 13-module curriculum as students in Uncodemy's classroom programs. Sessions run in real time, so you can raise questions, share your screen for code review and discuss RAG or agent architecture with the trainer during class.

Studying online also removes the need to relocate or commute to a metro for training — a practical advantage for working professionals in Sikar. 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. Fees and curriculum are identical in both modes.

The course runs for 5-6 months. You attend 100+ live online sessions spread across 13 modules, totalling 177 verified learning hours. Weekday and weekend batches are both available, so you can pick a schedule that fits alongside work or study.

Basic programming or Python familiarity helps, but prior coding experience is not mandatory. Python, SQL, Git and API engineering are taught from Module 2 onward, so learners with fundamental exposure can follow comfortably. A laptop and a stable internet connection are required.

You will work with 20+ tools: 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. Module 6 covers RAG & Enterprise Knowledge Systems (15 hours) and Module 7 covers AI Agents, Tool Calling & MCP (15 hours). Both are hands-on modules that include project work.

The capstone is Module 13, Enterprise AI Deployment & Capstone Project, spanning 25 hours. You build an end-to-end enterprise AI deployment — RAG pipeline plus AI agent, cloud deployment with CI/CD, and evaluation/guardrails — then present and defend it.

Classes are delivered live — 100+ sessions in total. Recordings are provided for revision so you can review any session or catch up on a class you could not attend.

The program fee is ₹19,500 plus 18% GST, totalling ₹23,010. The same fee applies in every mode and location — live online from Sikar or classroom at Delhi/Noida — with nothing charged beyond this.

Yes. EMI starts at ₹3,835 per month for 6 months, splitting the ₹23,010 total into instalments. *EMI amount, tenure and eligibility may vary depending on the applicable payment or financing option.

You may request a demo by completing the enquiry form or contacting our counsellors directly. Whether a demo can be arranged depends on the batch currently running, and the team will share the upcoming options.

Dedicated Career & Placement Assistance is included in the program, covering resume and LinkedIn help, mock interviews and access to openings through 850+ hiring partners. This support does not guarantee a job.

The program prepares you to apply for entry-level AI Forward Deployed Engineer roles, with indicative pay of ₹6–10 LPA. Securing an offer depends on your project work, interview performance and market conditions, and Uncodemy makes no guarantee of a specific role or salary.

Indicative ranges begin at ₹6–10 LPA for an Entry-Level AI Forward Deployed Engineer and reach ₹20–35 LPA for an AI Solutions Architect. Current India reporting places FDE salaries broadly around ₹9–27 LPA (source: Express Computer). Treat these as market ranges — no specific salary is guaranteed.

You become eligible for Forward Deployed Engineer, AI Solutions Engineer, Applied AI Engineer, AI Integration Engineer, Entry-Level AI Forward Deployed Engineer and (with experience) Senior Forward Deployed Engineer roles. The curriculum is broad enough to follow whichever path aligns best with your projects.

The course is open to software and IT professionals, freshers with programming fundamentals, data and ML engineers, career changers from technical consulting backgrounds, and working professionals wanting hands-on RAG/agents/MCP expertise. A laptop and stable internet are the only practical requirements.

No. Salaries listed are indicative market figures, placement support is not a job guarantee, and review counts come from public platform information that may change over time. All of it is presented for transparency so you can decide with full context.

No. There is no centre in Sikar — Sikar learners join this program live online. Uncodemy's classroom centres are in Delhi (Laxmi Nagar/Shakarpur) and Noida (Sector 1), and in-person training is available at either if you can travel.

This course is a broader, client-facing enterprise-deployment program covering discovery, RAG, agents, MCP, evaluation and deployment. LLM Engineering and RAG Engineering are narrower, deeper-technical specialisations focused on their specific domains.