01
Keep
The engineering or delivery strengths that remain useful in an AI role.
For experienced technology professionals
Find the AI role that builds on the career you already have.
You may already have part of the experience required for an AI engineering role. Start with the work you do today. See which AI paths fit your background, what skills you still need and what you should build to prove them.
Get My AI Career Map
5 questions. About 2 minutes. Free.
See Example Career Paths
Built for working technology professionals. Your current experience is part of the assessment.
Small live cohort
Live online delivery
Direct technical feedback
Role-aware learning path
Start with your current career
Which AI path makes the best use of what you already know?
Your job title tells only part of the story. Answer five questions about the work you do, your experience and the direction you want next.
We will use those answers to show a likely AI career path, the strengths that transfer and the gaps worth closing first.
See how the route changes
Different careers create different starting points.
These examples show why there is no single “AI roadmap” for every technology professional.
Your experience comes firstBuild from the work you already know
One serious buildArchitecture, deployment and defence
Direct feedbackCode, architecture and communication
Career translationResume and interview preparation
How your map is built
We separate what you already have from what you still need.
02
Add
The AI capabilities your likely target role expects.
03
Prove
The project evidence that can demonstrate those capabilities.
04
Target
The roles where your combined experience makes sense.
The goal is a shorter transition path based on evidence from your existing career.
How the programme works
Learn enterprise AI fundamentals. Apply them to your target role.
Your current role and target direction shape the assignments, project reviews and hiring preparation from the beginning.
Career Map and starting-point review
Start with your current work and likely AI direction. Review the strengths you can reuse and the gaps that deserve attention first.
Role-focused preparation
Compare your starting point with the work expected in the roles you are considering.
Common core with personal depth
The cohort shares enterprise AI fundamentals. Your assignments and review depth reflect your starting point and target.
Build and defend
You build a production-style system, explain the architecture and respond to questions about failure modes, cost and trade-offs.
Translate capability into hiring proof
Your project work is connected to resume language, technical stories and interview communication for the role you want.
Meet your instructor
Gaurav S
Now Director of AI at Leading MNC
Ex-HCL • Ex-Wipro • Ex-Accenture • Ex-HP
Review Programme SupportWhat Gaurav S works through with you
Your current technical strengths
The role you want to move toward
Your project and architecture decisions
How you explain those decisions in interviews
Programme support
Support through the programme, without outcome promises.
Enrolled learners in applicable programmes receive placement assistance, resume review, weekly interview preparation, and lifetime access to programme materials and recordings. Terms apply.
Placement assistance
Role-search guidance aligned with your programme and current experience.
Resume review
Feedback on how your skills and project work are presented.
Weekly interview preparation
Practice and feedback during the programme schedule.
Lifetime materials access
Programme materials and recordings remain available after the cohort.
A small cohort by design
A small cohort leaves room for role and project reviews.
Small live cohortRoom for individual reviews inside a shared live programme.
Small by design
The instructor should know more than your name.
In a BAIC cohort, the instructor should know your current role, the systems you have worked on, where your technical confidence is weak and which job you are trying to earn.
01
Relevant questions
Feedback can refer to your actual experience rather than a generic learner profile.
02
Visible progress
Code, architecture and communication gaps are easier to identify when the instructor sees the work repeatedly.
03
Peer range without crowding
You learn how adjacent roles approach the same AI system while retaining space for individual review.
04
Accountability
Your build has milestones, review moments and a final defence. Passive completion is not the goal.
Enterprise AI programme
A focused sequence from role clarity to hiring proof.
The programme is designed for working professionals who already have a technical base. Preparation may be useful for learners strengthening Python or application engineering foundations.
1
Start
Starting-point review
Review current work, target job descriptions and technical readiness. Identify foundations to strengthen before the core sequence.
2
Module 1
Target role and enterprise AI architecture
Understand the work expected in AI Engineer and FDE-type roles. Convert a business problem into system requirements, components and measurable success criteria.
3
Module 2
LLM applications and retrieval systems
Build beyond prompt demos. Work with APIs, structured output, embeddings, retrieval, context design and source-grounded responses.
4
Module 3
Agents, tools and controlled workflows
Design systems that call tools, manage state and handle multi-step tasks. Decide when an agent is useful and when a simpler workflow is safer.
5
Module 4
Evaluation, guardrails and deployment
Measure output quality, test failure cases, manage access and reduce risk. Prepare the system for deployment, monitoring and cost control.
6
Module 5
Client discovery and technical communication
Clarify ambiguous requirements, communicate trade-offs and present architecture to technical or business stakeholders. This is central for FDE and solutions-facing roles.
7
Module 6
Capstone defence and interview translation
Demonstrate the system, defend decisions and convert the work into credible resume language and technical interview stories.
Common core, personalised depth
You need a coherent system, not a pile of tools.
Tools will change. The programme focuses on the engineering decisions underneath them: requirements, architecture, retrieval quality, workflow control, evaluation, deployment and communication.
01
Python and API engineering for AI
The level depends on your background. The aim is to build dependable application logic around models.
02
LLM application architecture
Model access, structured output, context management, latency and cost-aware system design.
03
RAG and enterprise knowledge systems
Ingestion, chunking, retrieval, citation behaviour and evaluation over business knowledge.
04
Agentic workflows and tool use
State, tools, orchestration, failure handling and the boundary between autonomy and control.
05
Evaluation and guardrails
Golden datasets, quality measures, prompt injection awareness and safe response design.
06
Deployment, monitoring and cost
Packaging, cloud deployment, logs, traces, feedback loops and operational trade-offs.
07
Problem discovery and solution scoping
Translate a business problem into a buildable AI use case with clear acceptance criteria.
08
Architecture defence and interviews
Explain design choices, limitations and failure modes under questioning.
The proof-of-capability project
Build one enterprise AI system you can defend.
Your project should show that you can move from an unclear business need to a working system with measured behaviour. A notebook that produces an answer is not enough.
Business problem and success criteria
Data or knowledge ingestion
Retrieval and tool workflows
Evaluation and failure testing
Guardrails and access decisions
Deployment and observability
Architecture document
System walk-through and technical defence
System view
Enterprise documents
Operational data
User request
↓
Ingestion and access
AI orchestration layer
Tools and APIs
↓
Retrieval
Evaluation and guardrails
Logs and monitoring
↓
API or interface
Measured output
Feedback loop
Your final architecture depends on your chosen problem and target role. This diagram shows the expected level of system thinking.
What the programme is designed to produce
Evidence you can discuss with a hiring manager.
The programme focuses on capability that can be inspected, questioned and explained.
✓
Refined AI Career Map
Your free Career Map is the starting point. Inside the programme, the path is refined against your technical readiness, project work and the roles you intend to pursue.
✓
Enterprise AI project
Build an enterprise AI system with a repository, working demonstration and clear system boundaries.
✓
Architecture narrative
A structured explanation of requirements, design choices, trade-offs and failure modes.
✓
Code and architecture feedback
Direct review focused on whether the work supports the role you are targeting.
✓
Deployment thinking
Learn what moving a system beyond a local demonstration requires, including operational concerns.
✓
Resume and interview preparation
Translate project decisions and prior experience into credible role-specific stories.
Designed for working professionals
Built for a focused move into enterprise AI.
The programme suits experienced professionals with a technical base and a clear career transition goal.
Strong fit
- You have roughly 3-12 years in software, backend, cloud, DevOps, data, technical consulting or solution architecture.
- You want to move toward AI engineering, AI platform work, FDE-type work or another production-focused enterprise AI role.
- You can commit time each week to build, receive feedback and revise your work.
- You value direct instructor access and a plan tied to your career history.
Probably not the right fit
- You are a fresher seeking a first programming course or broad computer science foundation.
- Your main goal is an IIT, university or formally accredited academic credential.
- You want a passive video library without deadlines, reviews or project accountability.
- You require a guaranteed placement, promotion or salary outcome.
Start with your current career
Get a clearer AI direction before you choose a programme.
Five questions connect your current experience, preferred work and AI exposure to a practical next path.
You will see the strengths you can reuse, the gaps worth closing and a proof project that fits your direction.
Get My AI Career MapWhat your map brings togetherYour starting point
Current workThe systems, data, cloud or delivery work you already handle
Target directionThe AI role or kind of work you want to move toward
Transferable strengthsThe experience that remains valuable in an AI role
Priority gapsThe capabilities worth adding before you target the role
Proof projectA practical build that can demonstrate your capability
Questions before you apply
Questions about the programme.
BAIC is a live, small-cohort professional training programme for experienced technology professionals moving toward enterprise AI work. It combines shared technical foundations with role-aware project and interview guidance. It is not a university degree programme.
The cap keeps space for the instructor to understand your current role, target role and project decisions.
The cohort shares a common enterprise AI core. Assignment emphasis, project choices, review depth and hiring preparation can reflect your starting point and target role.
The programme is designed mainly for professionals with roughly 3-12 years of experience in software, backend, cloud, DevOps, data, technical consulting or solution architecture. Fit is assessed individually because job titles do not always reflect technical depth.
We will explain the Python and application-engineering foundations to strengthen before you join the programme.
Potential target roles include Enterprise AI Engineer, GenAI or LLM Application Engineer, Forward Deployed Engineer and AI Solutions Engineer. The realistic target depends on your prior experience and the market requirements for each role.
The programme is live online. Exact session timings and the catch-up process are shared before enrolment.
Programme timing and any recommended preparation are shared before enrolment.
The programme scope and fee are shared on the introductory call. You receive both before making an enrolment decision.
No. BAIC supports skill development, project work, role understanding and interview preparation. Employment outcomes depend on the learner, the hiring market and employer decisions.
BAIC reviews your background and target outcome, then contacts you about the programme and next steps. There is no obligation to enrol after the call.