Sr AI Architect

We’re on hunt for AI Architect

Responsibilities:

• 10–15+ years overall experience, with recent hands-on AI/GenAI architecture ownership.
• Must have architected enterprise AI platforms/solutions end-to-end, not just individual ML models or PoCs.
• Strong GenAI/LLM production experience: RAG, embeddings, vector DBs, hybrid search, reranking, evaluation, guardrails.
• Strong Agentic AI understanding: agents, tool calling, workflows, orchestration, human-in-the-loop.
• Experience taking AI solutions from architecture → production → scale, ideally across multiple business teams/use cases.
• Strong cloud architecture — Azure/AWS preferred; hybrid/on-prem experience is a plus.
• Must understand enterprise security, governance, Responsible AI, observability and LLMOps/MLOps.
• Should be able to articulate build-vs-buy, MVP-vs-target architecture, cost/performance/security tradeoffs.
• Strong stakeholder-facing / consulting ability — can work with business leaders, engineering, security and data teams and influence without authority.

There is scope to move to the US for this role if you are aligned for the same, else this will be a WFO role from Hyderabad location

Principal Enterprise GenAI / Agentic AI Architect - (Freelance) 

Positions: 1

Experience: Ideally 10–16 years overall, with significant recent hands-on GenAI/LLM architecture experience.

Mission

Own the end-to-end architecture across RAG, Agentic AI, enterprise integrations, model serving, security, evaluation, observability and production deployment.

This should not be a PowerPoint-only architect. We need someone technically deep enough to review code, challenge engineering decisions, troubleshoot RAG/agent behaviour and interact credibly with customer architecture/security/platform teams.

Mandatory capabilities

• Enterprise GenAI architecture
• Production RAG
• Agentic AI architecture
• Python
• LangGraph or comparable stateful orchestration
• Tool/function calling
• Human-in-the-loop workflows
• Vector databases
• Embeddings/reranking
• LLM/RAG/agent evaluation
• REST APIs/microservices
• Enterprise IAM
• RBAC/ABAC
• AI security and prompt-injection mitigation
• Kubernetes
• CI/CD and LLMOps/MLOps
• Enterprise observability

Highly desirable

OpenShift/OpenShift AI, NVIDIA NIM, KServe, NVIDIA GPU Operator, open-weight LLM deployment, ServiceNow, Splunk, Microsoft Graph and previous banking/financial-services experience.

Location: Hyderabad, India. Based at the KnackLabs headquarters, with occasional travel to client locations for workshops and reviews. This role does not involve extended onsite deployments.

About the Role

You will work as an AI Architect who designs the systems behind our client engagements: AI agents, RAG systems, automation platforms, and the conventional backend systems around them.

This is a hands-on design role, not a slideware role. You will scope architectures with clients, make the hard technical decisions, defend them in review, and stay accountable for how the systems perform in production.

You will work directly with clients. Everyone at KnackLabs does. You will sit in design discussions with client engineering teams, present architecture decisions to technical and business stakeholders, and answer for the choices you make.

A full KnackLabs engineering team in Hyderabad builds with you. You own the technical design and the quality of what ships.

What you'll own

1. Architecture - Design AI agents, RAG systems, integrations, and the scalable backend systems around them, for multiple client engagements.
2. Technical scoping - Work directly with clients to turn a business problem into a system design, with clear trade-offs and clear reasons.
3. Scale and reliability - Make sure what we build handles real load: data stores, queues, caching, horizontal scaling, and fault tolerance.
4. Design reviews - Review designs and builds across engagements. Set the technical bar and hold it.
5. Evaluation strategy - Define how we measure accuracy, safety, latency, and cost for the AI systems we ship.
6. Guiding engineers - Raise the level of the engineers building with you, through reviews and direct pairing.
7. Feedback to the platform - Feed what you learn across engagements back into our platform and internal tools.

What we are looking for

1. Around 7 or more years of software engineering experience, including direct work with customers on design or delivery.
2. Full-stack development experience with strength in backend technologies.
3. Experience designing and building scalable applications. You understand how large-scale distributed systems work: data partitioning, queues, caching, horizontal scaling, and fault tolerance.
4. At least 2 years of strong, hands-on AI experience with large language models in production.
5. You build with AI coding tools like Claude Code or Codex as your default way of working. You understand Claude Skills, have written skills yourself, use them actively, and have contributed to them.
6. Hands-on experience building retrieval-augmented generation (RAG) systems: chunking, embeddings, vector databases, retrieval, and reranking.
7. Hands-on experience building AI agents.
8. Strong programming skills in Python. Working knowledge of TypeScript or JavaScript.
9. Experience with at least one cloud platform (AWS, Azure, or GCP).
10. Clear communication. You can explain an architecture decision to an engineer and to a business leader, and defend it under questioning.
11. High ownership and comfort with ambiguity. You can take an unclear problem and turn it into a design.

Nice to have

1. Experience building evaluations to measure accuracy, safety, latency, and cost.
2. Experience with observability and tracing tools such as LangSmith or Braintrust.
3. Experience with on-premises or private cloud (VPC) deployments.
4. Experience deploying AI systems in regulated industries such as insurance, banking, or the public sector.
5. Experience with data engineering and pipelines.
6. A history of side projects, open source contributions, or products you shipped end-to-end.
7. Experience working at a consulting or professional services firm in a client-facing delivery role.

Stack and tools

1. Languages: Python and TypeScript.
2. Models: Claude and other frontier or open-source models, chosen to fit the customer.
3. AI patterns: RAG, agents, prompt engineering, skills, and evaluations.
4. Vector and retrieval: vector databases and retrieval pipelines.
5. Cloud: AWS, Azure, or GCP, on public or private cloud.
6. Integration: REST APIs and enterprise system connectors.

AI Developer  

Primary Skill-set (Must have) 

• Generative AI Expertise: 2-3 years of experience in designing and implementing generative AI solutions, including knowledge of various generative and autoregressive models. Ability to apply generative AI techniques to diverse use cases such as image generation, text generation, and creative content synthesis. 

• 2 years of experience in prompt engineering, fine tuning, agentic framework, GenAI SDK’s 

• 1-2 years of experience in Agentic AI frameworks like Autogen, Lanngraph, MS Agent SDK, A2A, MCP, A2P, memory concepts, multi agent orchestration 

• 7+ years of experience in Python 

• 5+ years of experience in software development 

• Azure Proficiency: 3-5 years of experience with Azure cloud services relevant to AI, including Azure Machine Learning, Azure Cognitive Services, Azure Databricks, and Azure Kubernetes Service (AKS). 2+ years of experience in Azure's capabilities to architect end-to-end AI solutions and optimize performance. 

• Architecture Design: 3-5 years of skills with the ability to design scalable, reliable, and cost-effective architectures for AI solutions. Proficiency in designing distributed systems, microservices architectures, and containerized solutions using technologies such as Docker and Kubernetes. 

Secondary Skills (Good to have) 

• Security and Compliance: Understanding of security principles and best practices in AI development, with the ability to implement security controls, encryption mechanisms, and access management policies to protect AI models and sensitive data. 

• Integration and Deployment: Proficiency in implementing CI/CD pipelines, automation scripts, and infrastructure as code (IaC) using tools such as Azure DevOps, Terraform, or Ansible. Experience in containerization and orchestration of AI workloads using Docker and Kubernetes. 

• Software Development: Strong programming skills in languages such as Python, with experience in developing AI applications, RESTful APIs, and microservices architectures. Familiarity with software development methodologies such as Agile or Scrum. 

• Communication and Presentation: Excellent communication skills with the ability to convey complex technical concepts to non-technical stakeholders. Experience in preparing and delivering technical presentations, architecture diagrams, and documentation to communicate architectural decisions and design rationale effectively. 

Support with design and build to prove out agentic AI solution flow by working with other data 

scientists and engineers to build, train Large Language Model (LLM) architectures, RAG 

systems, and autonomous agentic workflows 

Key qualifications: 

>> AI solution design & Development: Design Agentic AI solutions using RAG (Retrieval-

Augmented Generation) and orchestration frameworks like LangGraph or LangChain. 

>> Model Fine-Tuning: Solid understanding and experience with Pre-train, fine-tune, and 

optimize open-source like BERT, LLama, and other proprietary foundation models for domain-

specific tasks 

>> Solid Stats and ML foundations and (vibe) coding skills with Python, PySpark 

>>  Implement validation frameworks and tracing practices (using tools like Arize) to monitor 

agent behavior, guard against model drift, and ensure compliance 

>> Collaborate with Engineering to deploy models securely on cloud and on-prem ecosystems 

Design and develop Agentic AI systems using LLMs, tools, memory,

workflows, and MCP.

Build production-grade RAG pipelines, including ingestion, chunking,

embeddings, retrieval, reranking, and evaluation.

Implement context engineering strategies for improving LLM accuracy,

relevance, and reliability.

Develop and integrate MCP-based tools and services for AI agents.

Work with LLMs, SLMs, quantized models, and model optimization

techniques for efficient inference.

Develop scalable backend services and APIs for AI applications.

Design databases and data models supporting AI/agentic applications.

Implement AI observability covering latency, token usage, cost, failures,

quality, and agent/tool execution.

Apply AI governance and responsible AI practices, including security,

access control, data privacy, and auditability.

Optimize AI systems for latency, scalability, cost, and reliability.

Collaborate with engineering and product teams to take AI solutions from

POC to production.

Strong hands-on experience with GenAI, LLMs, and Agentic AI.

Experience building RAG applications.

Strong understanding of Context Engineering and prompt/context

optimization.

Role Overview

We are looking for a hands-on AI/ML Engineer to design, develop, and deploy

production-ready GenAI and Agentic AI applications. The role involves building

intelligent agents, RAG pipelines, AI APIs, backend services, and scalable AI

infrastructure with a strong focus on context engineering, observability,

governance, and model optimisation.

Key Responsibilities

Required Skills

Practical experience with MCP (Model Context Protocol).

Experience with frameworks such as LangChain, LangGraph,

LlamaIndex, or equivalent.

Knowledge of LLM/SLM deployment and quantization techniques.

Strong Python backend development experience.

Experience developing REST APIs using FastAPI/Flask or equivalent.

Strong understanding of SQL/NoSQL databases and database design.

Experience with vector databases such as Qdrant, Pinecone, Weaviate,

ChromaDB, or FAISS.

Understanding of AI observability, evaluation, monitoring, and

governance.

Experience with cloud platforms and production deployment is preferred.

Strong understanding of software engineering principles, Git, testing, and

CI/CD.

Senior AI Engineer

Code Generation, Agent Architecture & LLM Systems

📍 Mumbai (On-site) | Full-time | 5+ years

About the Role:

Unico Connect is an AI-first technology partner that builds custom mobile, web, and AI products for clients across multiple geographies.

We are hiring a Senior AI Engineer for a dedicated client engagement focused on building an AI-powered application builder platform - a product where users describe software in plain English and the system generates, previews, and iteratively refines working code.

The mandatory requirement for this role is hands-on production experience shipping LLM-powered systems with agent architectures, with experience in code generation or developer tooling contexts a strong advantage.

The role is product-focused and deeply hands-on. You will own everything between the user's prompt and correct code landing in the project: the agentic loop, code generation pipeline, context management, evaluation suite, and model cost strategy.

You will work alongside the Senior MLOps Engineer who operationalises the infrastructure around your system, and collaborate closely with backend, frontend, and DevOps engineers.

Responsibilities:

Agent Architecture

Design and own the agentic loop for the platform - request interpretation, planning, tool-calling sequence (read file, edit file, run build, search code, install package), and stop conditions.

Make and revisit architectural decisions on single-agent vs. multi-agent designs, including planner/executor splits and dedicated build-repair sub-agents.

Code Generation Pipeline

Own the end-to-end generation flow: task classification, context gathering, planning, targeted edits, verification, and commit.

Implement diff/search-replace-based file editing with fuzzy matching and fallback strategies.

Enforce scope discipline so the agent makes minimal diffs and does not modify code it was not asked to touch.

Self-Repair Loop

Build and tune the automated repair loop that pipes compiler, lint, build, and runtime errors back to the model with retry budgets and model escalation.

This loop is the primary quality lever - the difference between 60-70% and 90%+ build success rates.

Context Management

Build file-relevance retrieval so the agent sees the right files, not the whole codebase: dependency graphs, AST/tree-sitter-based chunking, embeddings, recency signals, and hybrid retrieval.

Implement conversation summarisation and memory for long sessions, and address long-project degradation through codebase summaries and periodic consistency passes.

Own token budgeting and prompt caching strategy.

Prompt Engineering as a Discipline

Own the system prompt and per-task prompt variants (new feature, bug fix, styling change).

Maintain few-shot examples and enforce coding conventions, stack rules, and prohibited behaviours such as no hardcoded secrets and no whole-file rewrites.

Version prompts like code with changelogs and rollback capability.

Evaluation and Quality Measurement

Design and own the evaluation suite: representative test prompts run on every prompt and model change, scored on build success rate, instruction adherence, and output quality including LLM-as-judge and visual/screenshot checks where relevant.

Define regression gates that block quality-degrading changes from shipping.

Treat evals the way engineers treat automated testing: versioned, automated, and tracked over time.

This responsibility is non-negotiable at this level.

Model Strategy and Cost

Design model routing - cheap and fast models for classification and small edits, frontier models for complex generation.

Drive cost optimisation through prompt caching, diff-based edits over full-file rewrites, and tighter context selection.

Track cost per agent run and tokens per task; evaluate new model releases against the eval suite and lead migrations when results justify it.

Safety and Reliability of Agent Behaviour

Defend against prompt injection from user content and fetched web content.

Ensure secrets never appear in generated client code.

Define what the agent's tools may and may not do in collaboration with the platform team.

Contribute to output moderation and abuse-pattern awareness.

Mentorship and Engineering Standards

Run code reviews, define engineering conventions for AI work, and raise the engineering bar across the AI team.

Work closely with the Senior MLOps Engineer on handoff of eval design, prompt configurations, and model routing logic.

Requirements:

Hands-on Production Ownership of LLM-Powered Systems with Agent Architectures (Mandatory)

Must have personally shipped and operated at least one complex production AI system - agentic, multi-step, or code generation - with end-to-end ownership of architecture, evaluation, and cost.

POCs, internal demos, and tutorial-grade work do not qualify.

5+ Years of Professional Software or AI Engineering Experience

With at least 3 years focused on LLM applications, AI engineering, or production AI systems.

Candidates with strong backend backgrounds and a clear, substantive pivot into LLM systems qualify.

Strong Python Proficiency and Service Development

Production-grade Python with FastAPI or equivalent: type hints, async patterns, streaming responses, testing, and packaging.

Not notebook-only.

Depth Across LLM APIs and Agent Systems

Production experience with at least two of OpenAI, Anthropic Claude, Google Gemini, or open-weight models (vLLM, Ollama, Together).

Production experience with at least one agent framework (LangGraph, CrewAI, AutoGen, LlamaIndex Agents) or hand-rolled equivalent.

Hands-on with tool calling, structured outputs, and multi-step reasoning.

Demonstrated, Systematic Evaluation Practice - Non-Negotiable

Must have built evaluation harnesses that gate production releases, not ad-hoc testing.

Hands-on with at least one of LangSmith, Langfuse, Promptfoo, Ragas, or DeepEval.

Candidates with no systematic answer to evaluation should not be considered at senior level regardless of other strengths.

Cost Discipline for Production AI

Track record of measurable cost optimisation on production AI features.

Able to speak in specifics: cost per request, savings achieved through caching or model routing, context reduction decisions.

AWS Working Knowledge

Hands-on with EC2, S3, IAM, and Docker.

Comfort with CI/CD workflows and deploying AI services.

Awareness of LLM Security Failure Modes

Familiar with prompt injection patterns, understands that system prompt rules alone are insufficient, and has experience with output validation and content safety in production.

Nice to Have

• Experience with AST/tree-sitter tooling, diff-based editing systems, or compiler-adjacent work
• MCP server authoring
• Open-source AI contributions
• Published technical writing on LLM systems
• Multi-modal model experience
• Fine-tuning exposure (LoRA, QLoRA, PEFT)

Key Responsibilities:

·      Architectural Leadership: Design and lead the development of robust, scalable AI architectures, ensuring high performance, reliability, and security.

·      Applied Mathematics & Statistics: Apply statistical analysis, numerical computation, and mathematical modeling to derive insights from large-scale data and optimize model performance.

·      Deep Learning Development: Design, train, and deploy advanced Deep Learning (DL) models.

·      Technical Mentorship: Mentor engineering teams on best practices for AI/ML, coding standards, and architectural design.

·      Model Optimization: Optimize models for speed, efficiency, and accuracy using techniques like pruning, quantization, or GPU acceleration.

·      Strategy & Innovation: Evaluate and select appropriate AI frameworks, tools, and platforms, staying abreast of cutting-edge research and industry trends.

Qualifications:

Required:

·      Education: Master's or PhD in Computer Science, Applied Mathematics, Statistics, Physics, or a related quantitative field.

·      Experience: 10+ years of experience in software development, with at least 3-5 years in a Applied Mathematics and Deep learning.

·      AI/ML Expertise: Proven experience designing and deploying deep learning models in production using frameworks.

·      Mathematics/Statistics: Strong proficiency in linear algebra, calculus, probability, and statistical methods.

·      Programming Skills: Expert-level coding skills in Python (NumPy, Pandas, Scikit-learn) and experience with languages like Java or C++.

Key Competencies:

• Strategic mindset with deep operational awareness.
• Excellent communication and stakeholder management skills.
• Ability to simplify complex technical concepts for executive reporting.
• Strong leadership, people development, and cross-functional influencing skills.

Bias for action and a relentless focus on continuous improvement.

Role Overview

The Principal Architect leads Byteridge’s Technology Strategy & Solutions Group (TSS). This is a senior, visible role responsible for defining technology point-of-view, shaping solution narratives, guiding enterprise conversations, and influencing revenue through differentiated thinking.

The Architect owns thought leadership, reference architectures, solution accelerators, and selective engagement on high-impact deals.

Key Responsibilities

• Own, enhance & execute Byteridge’s technology strategy across priority areas (Cloud, Data, Gen AI, Modernization).
• Create and maintain enterprise-grade reference architectures, solution blueprints, PoCs and accelerators.
• Lead strategic discovery workshops and executive-level solutioning for priority opportunities.
• Partner with Content Marketing to translate technical POVs into blogs, whitepapers, decks, webinars, and sales narratives.
• Enable the Enterprise Account Executive with differentiated solution stories and technical credibility.
• Build strong partnerships with Byteridge delivery teams to identify high-impact solutions and projects that can be leveraged as compelling capability showcases for existing customers and prospective clients.
• Work with Delivery Team Architects to influence delivery standards and architectural consistency across teams.
• Research market and industry trends across technologies, popular enterprise solutions, and buyer adoption patterns. Go deep into selected domains and verticals to continuously refine Byteridge’s technology strategy, solution approaches, and positioning.
• Act as a visible external voice through talks, webinars, and published content.

Ideal Profile

• 13–20 years of experience across technology architecture, solutioning, or technology consulting roles.
• Demonstrated ability to research and synthesize market trends, emerging technologies, and popular enterprise solutions.
• Experience developing deep expertise in specific domains or industry verticals and translating that into solution strategies.
• Strong background in modern software engineering, cloud platforms, data, AI, and enterprise systems.
• Proven track record of influencing client decisions and shaping solution direction, not just designing systems.
• Comfortable working at the intersection of technology, business strategy, marketing, and sales.
• Excellent communication skills with executive presence and the ability to articulate complex ideas clearly.

Success Metrics (KPIs)

• Quarterly technology and market POVs produced and adopted internally or externally.
• Creation and reuse of reference architectures, solution frameworks, and accelerators across deals.
• Number of high-impact delivery projects converted into capability showcases and sales assets.
• Influence on strategic opportunities, measured through deal quality, size, and AE feedback.
• Thought leadership visibility through blogs, webinars, talks, or industry participation.
• Internal adoption of architectural standards and solution approaches by delivery teams.

Job Title: Chief Agentic Systems Architect

Location: Remote

Type: Contract

ROLE OVERVIEW

We are hiring a Chief Agentic Systems Architect to transform a 10+ year legacy codebase into a high‑velocity, agent‑operable architecture. This role sits at the intersection of software architecture, AI‑agent orchestration, and engineering governance. You will design system patterns, MCP interfaces, and cognitive context layers that allow LLMs and autonomous agents to safely refactor, test, and ship production code with minimal human intervention.

WHAT YOU’LL DO

1. AGENT‑OPERABLE SYSTEM ARCHITECTURE

• Decompose legacy monoliths into agent‑readable, modular systems with strict boundaries and single responsibility
• Lead incremental modernization using the Strangler Pattern, wrapping legacy logic in modern, contract‑driven interfaces
• Enforce SOLID principles, Dependency Injection, and Hexagonal Architecture to ensure deterministic AI execution and low regression risk

2. AGENTIC FRAMEWORK & MCP LEADERSHIP

• Architect and maintain the agent context layer: standardized Skills, Rules, and Commands for AI‑driven engineering workflows
• Build and operate Model Context Protocol (MCP) servers exposing legacy APIs, services, and databases as typed, secure, AI‑consumable tools
• Own contract‑first API design as the primary interface between human intent and autonomous agent execution

3. ENGINEERING GOVERNANCE & AI QUALITY CONTROL

• Act as architectural gatekeeper for AI‑generated pull requests, ensuring scalability, security, and long‑term maintainability
• Mandate test‑driven development (TDD) and characterization testing to preserve legacy behavior during refactoring
• Monitor and optimize agentic reasoning loops to balance cost, speed, and architectural integrity

WHAT WE’RE LOOKING FOR

• 6+ years in software architecture, platform engineering, or technical leadership
• Proven experience modernizing large, undocumented legacy systems
• Deep hands‑on expertise with TypeScript, .NET, and Node.js
• Strong background in API design, distributed systems, and modular architectures
• Practical experience with agentic development, MCP, LLM tooling, or AI‑assisted engineering
• Bias toward clean code, deterministic systems, and production‑grade AI

NICE TO HAVE

• Experience with remote‑first or globally distributed teams
• Background in SaaS transformations, scale‑ups, or private equity-backed environments
• Comfort operating in high‑ambiguity, high‑ownership settings