Specialist Program
AI Engineering Specialist
A 6-month advanced weekend track for senior engineers, tech leads, and architects. Build production-grade multi-agent systems, master LLM operations, and ship secure AI products, with placement support and a published price.
Apply for Specialist ProgramPhase 1: Foundations & Architecture
Module 1
Advanced LLM Mechanics & Prompt Engineering
- Understand transformer internals, tokenization, attention mechanisms, and model scaling laws
- Design robust prompt patterns: chain-of-thought, few-shot, ReAct, and structured generation
- Compare closed vs. open-source models and select the right model for each enterprise task
- Build reusable prompt templates and evaluation harnesses for consistent output quality
Module 2
Retrieval-Augmented Generation & Vector Databases
- Design high-performance chunking, embedding, and indexing strategies for enterprise documents
- Implement hybrid search, reranking, and metadata filtering for accurate retrieval
- Build end-to-end RAG pipelines with query rewriting, context assembly, and citation tracking
- Deploy vector stores at scale with sharding, replication, and access-control policies
Module 3
Agent Frameworks & Multi-Agent Orchestration
- Architect autonomous agents with memory, tools, planning, and reflection capabilities
- Orchestrate multi-agent workflows using LangGraph, CrewAI, and state-machine patterns
- Implement agent-to-agent communication, task delegation, and conflict-resolution loops
- Design observability and tracing hooks to debug complex agent interactions in production
Phase 2: Production Engineering
Module 4
LLM Operations, Observability & Cost Control
- Monitor latency, throughput, token usage, and error rates with production-grade dashboards
- Implement caching, batching, streaming, and fallback strategies for cost-efficient inference
- Version prompts, datasets, and models with MLflow-style experiment tracking
- Set up alerting, circuit breakers, and automated rollbacks for AI services
Module 5
Evaluation, Safety & Output Guardrails
- Build eval suites using unit tests, reference-based metrics, LLM-as-judge, and human feedback
- Detect and mitigate hallucinations, bias, toxicity, and prompt injection attacks
- Implement input/output guardrails, PII redaction, and audit logging for regulated industries
- Align systems with enterprise policies using fine-grained access control and content filters
Module 6
Fine-Tuning, Quantization & Model Optimization
- Prepare domain-specific datasets and run supervised fine-tuning with LoRA/QLoRA
- Quantize models for edge and cost-constrained deployment without major accuracy loss
- Distill knowledge from large teacher models into smaller, faster student models
- Evaluate trade-offs between model size, latency, cost, and task-specific performance
Phase 3: Enterprise Deployment
Module 7
Cloud Infrastructure & Containerization
- Containerize AI applications with Docker and orchestrate with Kubernetes
- Provision GPU/CPU inference nodes on AWS, GCP, and managed AI platforms
- Design auto-scaling, load balancing, and health-check strategies for model serving
- Implement infrastructure-as-code pipelines for reproducible AI deployments
Module 8
API Design, Security & Identity
- Build secure REST/gRPC APIs with rate limiting, authentication, and request validation
- Integrate SSO, RBAC, and API key management for enterprise tenants
- Protect against OWASP LLM Top 10 risks and implement secure secrets management
- Document APIs, generate SDKs, and expose usage analytics to enterprise customers
Module 9
Real-Time Pipelines & Event-Driven AI
- Stream data into AI services using Kafka, webhooks, and message queues
- Build long-running agent tasks with durable execution and retry semantics
- Connect AI workflows to Slack, email, CRM, and internal enterprise systems
- Design idempotent, scalable event handlers that survive failures and replays
Phase 4: Advanced Systems
Module 10
Custom AI Projects & Product Roadmapping
- Translate ambiguous business requirements into concrete AI product specifications
- Prototype, iterate, and de-risk ideas using rapid build-measure-learn cycles
- Define success metrics, data strategy, and rollout plans for internal AI tools
- Present technical proposals to non-technical stakeholders and secure buy-in
Module 11
Voice, Vision & Multimodal Systems
- Integrate speech-to-text, text-to-speech, and voice-agent orchestration
- Build vision pipelines for document parsing, object detection, and image understanding
- Combine text, image, audio, and structured data in unified agent workflows
- Optimize latency and cost for real-time multimodal applications
Module 12
AI Strategy, Governance & Ethics
- Build AI governance frameworks covering data privacy, IP, and compliance requirements
- Conduct risk assessments and maintain model cards for enterprise stakeholders
- Plan vendor selection, cost forecasting, and build-vs-buy decisions
- Lead responsible AI initiatives that balance innovation with societal impact
Phase 5: Career & Placement
Module 13
Leadership, Communication & Stakeholder Management
- Translate technical AI concepts for executives, product managers, and clients
- Run proof-of-concept reviews, retrospectives, and go/no-go decision meetings
- Build and mentor high-performing AI engineering teams
- Negotiate scope, timelines, and budgets for AI initiatives
Module 14
Portfolio, Interview Prep & Placement Support
- Polish GitHub repositories, case studies, and technical blogs for recruiter visibility
- Practice system-design interviews focused on AI architecture and scale
- Receive warm introductions to hiring partners and curated job opportunities
- Get 1:1 mentorship on salary negotiation and long-term career positioning
Phase 6: Capstone & Graduation
Module 15
Capstone Project & Production Launch
- Define, architect, and build a real-world AI product end-to-end with mentor guidance
- Deploy the capstone to production infrastructure with monitoring and documentation
- Present the project to industry reviewers and receive detailed feedback
- Graduate with a portfolio piece, certificate, and alumni network access
Outcomes
What will you be able to do?
By the end of the Specialist track, you will have the depth and breadth to lead AI engineering initiatives in top product companies and consultancies.
Architect Multi-Agent Systems
Design autonomous agent teams with memory, tools, planning, and secure orchestration for complex enterprise workflows.
Ship Production AI Products
Deploy RAG pipelines, fine-tuned models, and AI APIs on cloud-native infrastructure with monitoring and guardrails.
Build Secure & Compliant AI
Implement enterprise security, privacy controls, and governance practices aligned with global standards.
Measure & Optimize AI Systems
Run rigorous evaluations, control costs, and continuously improve model quality in live production environments.
Lead AI Strategy
Drive build-vs-buy decisions, roadmap AI products, and communicate trade-offs to executive stakeholders.
Land Senior AI Roles
Position yourself for AI engineer, staff engineer, and architect roles with a portfolio and placement support.
Who Should Join
Who should join?
This program is designed for experienced technologists who want to move beyond tutorials and into real AI engineering leadership.
Senior Software Engineers
You have 4+ years of backend, full-stack, or ML engineering experience and want to specialize in applied AI systems.
Tech Leads & Engineering Managers
You are expected to evaluate AI technologies, guide your team, and deliver AI-powered features to production.
Solution Architects
You design platforms for enterprise clients and need hands-on fluency with modern LLM and agent architectures.
Tools & Technologies
Which tools will you master?
Work with the same frameworks, databases, and platforms used by leading AI product teams.
LangChain & LangGraph
Agent chains, memory, and stateful orchestration
Prompt
→
Tool Call
→
Memory
→
Output
CrewAI
Multi-agent role-based collaboration
P
C
W
Pinecone & Weaviate
Vector search and retrieval systems
AWS / GCP
Cloud infrastructure, GPU compute, and managed AI services
S3
→
SageMaker
→
EKS
→
CloudWatch
Docker & Kubernetes
Containerization and scalable orchestration
ImageBuild
PodDeploy
ScaleAuto
LangSmith, W&B & OpenTelemetry
Observability, experiment tracking, and cost monitoring
# Production trace example
trace("agent.run")
.span("llm.call", tokens=1420)
.span("retriever.search", latency="120ms")
trace("agent.run")
.span("llm.call", tokens=1420)
.span("retriever.search", latency="120ms")
Investment
What does the Specialist track cost?
A focused investment in becoming a production-ready AI engineering specialist.
6-Month Specialist Track
₹3,00,000
One-time payment · Taxes applicable · EMI options available
- Live instructor-led sessions twice a week
- 1:1 mentorship and code reviews
- Real-world capstone project with production deployment
- Certificate of completion and placement support
- Lifetime access to alumni community and resources
FAQ
Questions about the Specialist program
Everything you need to know before applying.
Live sessions run twice a week on weekday evenings (India time), with additional office hours over the weekend. Each week combines theory, hands-on labs, and mentor-led project check-ins.
You should be comfortable with Python, Git, REST APIs, and basic system design. Prior exposure to machine learning or cloud concepts is helpful but not mandatory.
Please refer to our Refund Policy. In brief, full refunds are available within the cooling-off window specified before the cohort start date.
Yes. Fellows who complete the curriculum, capstone project, and final review receive a verified certificate from Rewire.
Specialist fellows receive resume and portfolio reviews, mock interviews, warm introductions to hiring partners, and access to curated job opportunities in AI engineering and architecture.
Ready to lead with AI?
Applications are reviewed on a rolling basis. Secure your seat in the next cohort.
Apply for Specialist Program