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

• Role of the Forward Deployed Engineer
• Enterprise AI value chain
• Business problem discovery frameworks
• Solution scoping and stakeholder alignment
• Production readiness criteria
• Client communication essentials
• Case studies of enterprise AI initiatives

• Python for data and AI workflows
• SQL querying and data extraction
• Git version control and collaboration
• REST API design and consumption
• FastAPI basics
• Environment management
• Debugging and testing practices

• Transformer architecture overview
• Large language model capabilities and limits
• Generative AI application patterns
• Tokenization and context windows
• OpenAI and Hugging Face model usage
• Cost and latency considerations
• Responsible AI principles

• Prompt design patterns
• Few-shot and chain-of-thought techniques
• Context window management
• System and user message strategies
• Prompt evaluation methods
• Dynamic context injection
• Production prompt versioning

• Enterprise data source types
• ETL/ELT patterns for AI
• Data cleaning and transformation
• Pipeline orchestration concepts
• Structured and unstructured data handling
• Security and access control
• Monitoring data pipelines

• Retrieval-Augmented Generation architecture
• Document ingestion and chunking
• Embedding models and vector stores
• Hybrid search strategies
• Knowledge base design
• RAG evaluation metrics
• Enterprise knowledge system patterns

• Agent architectures and planning
• Tool calling and function calling
• Model Context Protocol (MCP)
• Multi-agent coordination
• Memory and state management
• Agent reliability patterns
• Observability for agents

• LangChain application patterns
• LangGraph for complex flows
• LlamaIndex for knowledge applications
• FastAPI serving of AI endpoints
• Frontend integration considerations
• Error handling and retries
• Versioning AI applications

• Evaluation frameworks and metrics
• LLM-as-judge techniques
• RAGAS and related tools
• Tracing and observability platforms
• LangSmith / Langfuse usage
• Drift and performance monitoring
• Continuous evaluation pipelines

• Prompt injection and mitigation
• Guardrail design patterns
• Data privacy and compliance
• Access control for AI systems
• Output filtering and moderation
• Secure model serving
• Audit and logging requirements

• Containerization with Docker
• Cloud platforms (AWS / Azure overview)
• CI/CD pipelines for AI services
• Infrastructure as code basics
• Scaling and cost management
• Deployment strategies
• Production monitoring setup

• Discovery workshop techniques
• Requirements gathering for AI
• Solution architecture diagrams
• Scoping and estimation
• Stakeholder communication
• Consulting engagement models
• Risk and success criteria definition

• End-to-end project planning
• Client problem selection and scoping
• RAG / agent system implementation
• Evaluation and guardrail integration
• Cloud deployment and CI/CD
• Documentation and hand-over
• Capstone presentation and review

Forward Deployed Engineer roles are emerging across Agra's growing enterprise software, IT services and product-engineering landscape as organisations adopt applied AI. Local companies and service providers increasingly need professionals who can translate business problems into production AI systems rather than only building models.

Interviews for these positions typically emphasise system design for RAG and agent architectures, hands-on deployment experience, and the ability to work directly with client stakeholders on discovery and solution scoping. Agra's established base of enterprise software and IT-services firms provides a natural demand environment for engineers who combine technical depth with client-facing delivery skills.

Candidates who can demonstrate end-to-end ownership—from requirements gathering through cloud deployment and monitoring—stand out. The role suits professionals who enjoy both technical problem-solving and structured client engagement.

Graduates are positioned for a range of applied AI delivery roles that combine engineering with client-facing responsibilities. The table below maps common target roles to the core tools and capabilities developed in the program.

Over a two-to-five-year horizon, successful practitioners commonly progress toward AI Solutions Architect positions that involve broader technical leadership and solution design across multiple enterprise engagements.

Salary expectations for Forward Deployed Engineer and related roles in the Indian market, including Agra, follow the indicative ranges shown below. Actual compensation depends on individual skills, prior experience, employer and the specific technical scope of the role.

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.

Uncodemy's hiring network of 850+ companies spans multiple sectors that actively seek professionals with enterprise AI deployment skills. In and around Agra the following areas show particular relevance:

• Enterprise software and SaaS companies: Building AI-augmented products for business clients.
• IT services firms: Delivering applied AI solutions to domestic and international customers.
• Fintech organisations: Integrating AI into risk, operations and customer-facing workflows.
• Product engineering teams: Embedding RAG and agent capabilities into existing platforms.
• Consulting and system-integrator practices: Requiring client-facing AI delivery expertise.

These sectors value engineers who can move from problem discovery to reliable production systems.

A structured path from fundamentals to production deployment can be followed through the modules of this program.

1. Build a foundation in Forward Deployed Engineering and enterprise AI fundamentals.
2. Strengthen Python, SQL, Git and API engineering skills.
3. Master LLM fundamentals and generative AI application patterns.
4. Develop prompt engineering and context engineering proficiency.
5. Learn RAG architectures and enterprise knowledge system design.
6. Gain hands-on experience with AI agents, tool calling and MCP.
7. Complete cloud deployment, evaluation practices and the enterprise AI capstone.

The full program is designed to be completed in 5-6 months of live online study.

Prospective learners often compare this program with narrower technical specialisations. The table below summarises key differentiators.

Compared with Uncodemy's LLM Engineering or RAG Engineering courses, the AI Forward Deployed Engineer program is the broader, client-facing enterprise-deployment offering. It covers discovery, RAG, agents, MCP, evaluation, deployment and consulting. LLM Engineering and RAG Engineering remain narrower, deeper-technical specialisations suited to learners who already know they want to concentrate exclusively on those specific layers.

Agra learners join this program fully live online and receive the same trainers, curriculum, session quality and batch structure as participants in Uncodemy's classroom programs. Live interaction, real-time doubt resolution, collaborative project work and recorded sessions for revision are all available.

For those who prefer or require in-person attendance, classroom training continues to be offered at Uncodemy's established centres in Delhi (Laxmi Nagar / Shakarpur) and Noida (Sector 1). There is no physical Uncodemy classroom centre in Agra; the live-online format ensures Agra-based professionals can access the complete program without relocation while still having the option to travel to Delhi or Noida if they wish.

The program runs for 5-6 months. It includes more than 100 live sessions and 177 hours of structured learning delivered live online for Agra participants.

Basic programming familiarity is helpful but not mandatory. Python, SQL, Git and API engineering are taught from Module 2 onward, allowing learners to build the required foundation during the course.

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. Module 6 is dedicated to RAG and enterprise knowledge systems, and Module 7 covers AI agents, tool calling and MCP in depth.

The final module is a 25-hour Enterprise AI Deployment & Capstone Project in which learners plan, implement, evaluate, deploy and present an end-to-end enterprise AI solution.

All core sessions are live (100+ live sessions). Recordings are provided for revision so learners can revisit material at their own pace.

The program fee is ₹19,500 + 18% GST, making the total ₹23,010. The same fee applies across all modes and cities.

Yes. EMI starts from ₹3,835 per month for 6 months. EMI amount, tenure and eligibility may vary depending on the applicable payment or financing option.

Prospective learners can request information about upcoming orientation or demo sessions by contacting the Uncodemy team through the website or the listed phone numbers.

Yes. Dedicated Career & Placement Assistance is provided through a network of 850+ companies. This assistance does not constitute a guarantee of employment.

The indicative salary range for an Entry-Level AI Forward Deployed Engineer is ₹6–10 LPA. Actual outcomes depend on individual skills, experience and market conditions; no specific placement or salary is guaranteed.

Indicative ranges span from ₹6–10 LPA at entry level to ₹15–27 LPA for senior roles, with broader market reporting around ₹9–27 LPA (source: Express Computer). Salary ranges are indicative and should not be presented as guaranteed outcomes.

Common target roles include 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 and AI Solutions Architect.

Software and IT professionals, freshers with programming fundamentals, data/ML engineers, career changers from technical backgrounds and working professionals seeking RAG, agents and MCP expertise are all suitable candidates.

No. Statistics are based on internal records and publicly available platform information. Placement and salary outcomes vary by learner, skills, experience, location, employer and market conditions. Career assistance does not guarantee employment or a specific salary.

No. Agra learners join the program fully live online. Uncodemy's classroom centres are located in Delhi (Laxmi Nagar / Shakarpur) and Noida (Sector 1).

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 respective domains.