Job Overview:

We are seeking a Senior AI Solution Architect with deep expertise in Artificial Intelligence (AI) and Generative AI (GenAI) to drive solution design, pre-sales engagements, and client delivery. The ideal candidate combines technical excellence with strong business acumen, enabling them to design innovative solutions, craft compelling sales collateral, and confidently engage with executive stakeholders. This role requires proven experience leading teams, shaping solution strategy, and responding to complex RFPs in enterprise environments

Key Responsibilities:

• Lead RFP/RFI responses with strong technical and business narratives.
• Design solution proposals, demos, and workshops with sales teams.
• Build sales assets such as whitepapers, PoV decks, and blueprints.
• Engage C-suite leaders to evangelize AI/GenAI solutions.
• Define end-to-end AI/GenAI architectures (RAG, multi-agent, pipelines, governance, observability).
• Translate business needs into scalable, secure, and compliant AI solutions.
• Ensure smooth design-to-delivery with engineering teams.
• Establish frameworks for AI governance and responsible AI.
• Mentor architects, data scientists, and engineers.
• Drive knowledge sharing through playbooks, accelerators, and best practices.
• Collaborate with cloud, data, security, and domain SMEs for integrated solutions.

Key Skills

• 10+ years of experience in solution architecture, AI/ML, or data platforms, with at least 3–5 years in AI/GenAI initiatives.
• Demonstrated pre-sales experience: RFP responses, PoVs, workshops, and deal support.
• Strong portfolio of sales/marketing assets (whitepapers, solution blueprints, reference architectures).
• Hands-on expertise with AI/GenAI stacks (e.g., LangChain/LangGraph, CrewAI, OpenAI, Anthropic, Azure AI, AWS Bedrock, Databricks).
• Understanding of AI governance and compliance frameworks (EU AI Act, NIST AI RMF, ISO 42001, Microsoft RAI Maturity Model).
• Proven team leadership experience, including mentoring and managing architects/engineers.
• Excellent communication, presentation, and client-facing skills.

Role Overview:

We are looking for a hands-on Product Architect / Engineer to design and build an AI-driven operations platform focused on intelligent automation, insights, and optimization across cloud, network, and application operations.This role blends product thinking, architecture, and deep engineering across AI/ML and Agentic AI systems.

Key Responsibilities:

• Architect and build the agentic AI platform leveraging AI, ML, and Agentic AI frameworks
• Design multi-agent systems for use cases for operations domains like Cloud Ops, NetworkOps, ServiceDesk, root cause analysis, anomaly detection, and predictive insights
• Develop data pipelines and telemetry fabric for ingesting and processing large-scale operational data (logs, metrics, traces, topology)
• Build and integrate LLM-powered copilots and autonomous agents for operations 
• Define product architecture, including microservices, APIs, event-driven systems, and scalable cloud-native patterns
• Translate business needs into AI-driven product features and accelerators
• Collaborate with product, operations, and engineering teams to deliver end-to-end capabilities

Key Skills:

• Strong experience in product architecture and full-stack engineering
• Hands-on expertise in:
• AI/ML (model development, inference, MLOps)
• LLMs and Agentic AI frameworks (multi-agent orchestration, reasoning systems)
• Data engineering (streaming, batch, real-time analytics)
• Proficiency in Python and modern frameworks
• Experience with cloud platforms (AWS, Azure) and cloud-native architecture
• Agent framework experience – Microsoft agent framework, langgraph and langchain.
• Strong understanding of:
• Observability systems (logs, metrics, traces)
• AIOps / IT operations platforms
• API design and microservices architecture
• Familiarity with vector databases, embeddings, RAG architectures, and knowledge graphs

Good to Have:

• Experience building autonomous AI agents for IT/network/application operations
• Exposure to FinOps, Cloud Cost Optimization,
• Knowledge of network topology, infrastructure ops, or ITSM tools
• Experience with real-time decisioning systems and event correlation engines

Job Overview:

We are looking for a senior technical lead to design, scale, and govern enterprise-grade data platforms across Microsoft Fabric, Databricks, and Snowflake. The role focuses on defining data architecture standards, enabling AI/GenAI-ready platforms, leading engineering teams, and delivering secure, high-performance cloud data solutions that support analytics and business outcomes.

Key Responsibilities

• Lead the design and implementation of scalable data platforms using Microsoft Fabric, Databricks and Snowflake including Pipelines, Notebooks, Lakehouse, Data Warehouse, and Semantic Models.
• Define end-to-end data architecture standards, patterns, and best practices across ingestion, storage, transformation, modeling, and consumption layers.
• Guide technical decisions on compute patterns, storage optimization, pipeline orchestration, and data modeling frameworks.
• Evaluate and integrate emerging technologies to modernize the platform and enable AI/GenAI workloads.
• Partner with AI/ML teams to productionize models and integrate them with enterprise data workflows.
• Ensure data governance, lineage tracking, and metadata standards to support trustworthy AI initiatives.
• Champion data governance, security, privacy, and compliance requirements within the platform.
• Implement observability frameworks for pipeline monitoring, performance tuning, and reliability engineering.
• Drive continuous improvement initiatives to enhance platform robustness and engineering productivity.
• Work closely with product owners, business stakeholders, architects, and analytics teams to translate requirements into scalable technical solutions.
• Serve as the primary technical point of contact for data platform projects and stakeholders.
• Communicate technical roadmaps, project risks, and delivery plans clearly and effectively.
• Develop and deliver dashboards and reports using Power BI and Tableau.

Key Skills:

• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or equivalent professional experience.
• 12+ years of experience in data engineering, data platform development, or cloud data architecture.
• 3+ years in a technical leadership or team management role.
• Mandatory deep hands-on expertise with Microsoft Fabric, Snowflake or Databricks:
  • Pipelines
  • Notebooks
  • Lakehouse
  • Data Warehouse
  • Semantic Models
• Strong proficiency in PySpark, distributed compute, and data transformation frameworks.
• Advanced SQL capabilities and experience building stored procedures for transactional and analytical workloads.
• Proven track record in designing and delivering large-scale data platforms in cloud environments (Azure preferred).
• Solid understanding of AI/ML data workflows, GenAI dataset preparation, and integration patterns.
• Demonstrated ability to lead engineering teams, drive architectural decisions, and oversee complex technology implementations.
• Strong communication, stakeholder management, and technical documentation skills.
• Proficiency in creating interactive dashboards and reports using Power BI and Tableau.

Job Overview:

We are seeking a seasoned AI Developer to lead the design and implementation of next-generation Agentic AI solutions. You won't just be building chatbots; you will be architecting autonomous systems where multiple specialized AI agents collaborate to solve complex, long-horizon business problems. You will leverage a multi-cloud strategy to deploy scalable, resilient, and "reasoning-heavy" AI workflows.

 Key Responsibilities

• Agentic Architecture: Design and develop autonomous agents capable of independent planning, tool-use (function calling), context & prompt engineering, and self-correction.
• Multi-Agent Orchestration: Implement LangGraph to manage task delegation, conflict resolution, and collaborative reasoning between specialized agents.
• Advanced RAG & Memory: Build sophisticated Retrieval-Augmented Generation (RAG) pipelines with episodic and semantic memory to ensure agents maintain context over long interactions.
• Cloud-Native Deployment: Architect and deploy AI services across AWS (Bedrock/SageMaker), Azure (OpenAI Service/Foundry), ensuring high availability and cost optimization.
• Guardrails & Ethics: Implement rigorous evaluation frameworks (e.g., Ragas, TruLens) and safety guardrails to prevent hallucinations and ensure responsible agent behavior.
• Performance Tuning: Optimize LLM latency and throughput using techniques like prompt caching, quantization, and specialized model routing.
• Prompt engineering & Context engineering.

Key Skills:

• Core AI: 4+ years of professional experience in AI/ML, with at least 1.5+ years focused specifically on Generative AI and LLM orchestration.
• Agent Frameworks: Hands-on expertise with LangGraph. Proven ability to build "loops" and "state machines" rather than just linear chains.
• Programming: Mastery of Python (Asyncio, Pydantic, FastAPI). Experience with TypeScript/Node.js is a significant plus.
• Cloud Infrastructure:
• AWS: Bedrock, Lambda, Step Functions, S3.
• Azure: Azure OpenAI, AI Search, CosmosDB, PostgreSQL.
• Data & Memory: Proficiency with Vector Databases like Pinecone, Weaviate, Milvus, or pgvector. Understanding of graph databases (Neo4j) for knowledge-graph-enhanced RAG.
• DevOps/AIOps: Experience with Docker, Kubernetes, and CI/CD pipelines specifically for AI (LLMOps).

 Preferred Qualifications

• Contributions to open-source AI frameworks or research publications in MAS.
• Experience with Model Context Protocol (MCP) or Agent-to-Agent (A2A) communication standards.
• Familiarity with fine-tuning techniques (LoRA, QLoRA) for specialized agent tasks.

Job Overview:

We are seeking an experienced Azure Cloud Application Architect to design and implement enterprise-grade, cloud-native solutions using Azure services. This role involves defining best practices for architecture, governance, security, and cost optimization while enabling CI/CD and Infrastructure as Code. You will provide technical leadership, mentor teams, and act as a trusted advisor to clients, driving modernization, migration, and innovation initiatives. The position also includes pre-sales and post-sales engagement, building strategic partnerships, and contributing to thought leadership through white papers and presentations.

Experience: 10+ years

Key Responsibilities:

• Design and implement cloud-native application architectures using Azure services (App Services, AKS, Functions, Service Bus, etc.).
• Collaborate with stakeholders to translate business requirements into technical solutions.
• Establish governance, cost optimization, and security best practices for Azure applications.
• Work closely with DevOps teams to enable CI/CD pipelines and infrastructure as code (IaC).
• Provide technical leadership, mentoring, and guidance to development teams.      
• Define and implement best practices architecture design, deployment, and operations.
• Design highly available, scalable, and cost-effective cloud solutions meeting business requirements.
• Collaborate with cross-functional teams, including DevOps, development, and business units, to align cloud solutions with organizational goals.
• Analyse complex technical issues, provide recommendations, and lead the resolution efforts for cloud-related challenges.
• Build capabilities, offerings, service accelerators and differentiators.
• Build a go-to-market strategy that highlights our cloud offerings that best support our customers' challenges.
• Innovation and Thought leadership, bringing out white papers, blogs, presentations, and collaterals.
• Responsible for delivering solutions architecture for major clients.
• Client Relationship Management (both pre-sales, Solution & post-sales)
• Be client CIO/CTO's trusted advisor.
• Deliver superior value and conduct project reviews.
• Enable teams to deliver better value in projects.
• Coaching, Mentoring and Provide team a clear direction.

Should be hands on:

• Create and maintain architectural documentation, reports, and presentations for stakeholders.
• Define solution blueprints and oversee application modernization and migration to Azure.
• Pre-sales / post-sales (play an active role in winning business)
• Build and grow relationships with cloud and alliance partners to expand our relationship, offerings, and revenues.
• Build relationships with key client decision makers and influences in partnership with sales and marketing.

Key Skills:

• Expertise in designing high-level, enterprise grade solution architecture
• At least 10+ years of relevant experience as an Azure Cloud App Architect
• Ability to manage estimates, tasks, escalations, risks/issues
• Ability to clearly articulate concepts to people with varying technical background
• Azure Certified Solution Architect - Az 303/304/305.

Role: GenAI Lead Engineer

Experience: 8+ years total (with significant hands-on GenAI / LLM work)

Job Overview:

We are seeking an innovative and highly skilled Lead Generative AI (GenAI) Engineer to spearhead the design, development, and deployment of advanced AI-powered solutions. In this role, you will lead a team of engineers and data scientists to harness cutting-edge Generative AI technologies and implement them to solve complex business problems, enhance user experiences, and drive innovation. This role combines deep technical expertise, leadership, and a strong understanding of AI trends and tools.

Key Responsibilities:

• Technical Leadership:
• Lead the end-to-end design and implementation of Generative AI solutions.
• Provide technical guidance and mentorship to engineers and data scientists working on GenAI projects.
• Stay updated with the latest trends, research, and advancements in Generative AI and Large Language Models (LLMs).
• Solution Development:
• Architect, train, and fine-tune state-of-the-art LLMs and generative AI models
• Develop and optimize pipelines for prompt engineering, retrieval-augmented generation (RAG), and domain-specific fine-tuning.
• Develop and deploy generative AI models, particularly focusing on ChatGPT, using Python on Azure or AWS Platform or .Net on Azure platform
• Ensure scalability, performance, and security of AI solutions deployed in production.
• Integration and Deployment:
• API Development: Ability to define and deliver API access for GenAI services, facilitating integration with other systems and applications.
• Collaborate with software engineering teams to integrate GenAI solutions into enterprise applications and services.
• Utilize cloud platforms (e.g., Azure, AWS, or GCP) to deploy and manage AI models and APIs.
• Leverage MLOps practices for continuous model monitoring, retraining, and improvement.
• Data Strategy and Preparation:
• Collaborate with data engineering teams to ensure high-quality data acquisition, preprocessing, and augmentation for model training and fine-tuning.
• Implement data governance and privacy practices in line with organizational policies.
• Innovation and Research:
• Experiment with new generative AI techniques, such as multimodal AI, reinforcement learning with human feedback (RLHF), and active learning.
• Evaluate and recommend AI frameworks, libraries, and platforms for project requirements.
• Stakeholder Collaboration:
• Work closely with product managers, business stakeholders, and UX designers to define AI-powered product features and use cases.
• Present technical concepts, project progress, and AI capabilities to non-technical audiences.

Key Requirements

• Technical Skills:
• Hands-on experience with cloud platforms and services for AI/ML, such as Azure AI Services, Azure Machine Learning, AWS Bedrock, or Google Vertex AI.
• Hands on experience in any of LLMs such as OpenAI’s ChatGPT Models , Gemini, Llama 2 ,Claude 2 ,Grok
• Hands on experience in any of the agentic frameworks like LangChain, Semantic kernel, AutoGen, CrewAi
• Hands on experience using any of vector database like Chroma, Pinecone, Weaviate, Faiss
• Experience with multimodal AI and advanced techniques like Tree-of-Thoughts, Retrieval-Augmented Generation (RAG), or Reinforcement Learning with Human Feedback (RLHF)
• Strong expertise in LLMs and generative AI frameworks like OpenAI, Hugging Face Transformers, or similar platforms.
• Deep understanding of natural language processing (NLP) concepts, including tokenization, embeddings, and sequence-to-sequence models.
• Proficiency in Python and libraries such as TensorFlow, PyTorch, and Scikit-learn.
• Experience in CI/CD pipeline management and automation tools, particularly within the Azure DevOps environment. Knowledge of containerization (e.g., Docker) and orchestration tools is also important
• Familiarity with MLOps tools and practices, such as MLflow, Kubeflow, or Docker.

Qualifications:

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field (Ph.D. preferred).
• 8+ years of experience in AI/ML engineering, with 2+ years specifically in Generative AI.
• Minimum of 2 years of experience in building Conversational AI applications using cloud-based services and in orchestrating AI/ML services for building a complete solution
• Minimum of 6 years of extensive full-time experience in Data Analysis, Statistics, Machine Learning, or Computer Science
• Proven track record of leading AI projects from inception to production.
• Experience with multimodal AI and advanced techniques like Tree-of-Thoughts, Retrieval-Augmented Generation (RAG), or Reinforcement Learning with Human Feedback (RLHF).
• Certifications in AI/ML or cloud platforms (e.g., Azure AI Engineer, AWS Certified Machine Learning).

Job Overview:

We are looking for a Senior GenAI Developer to design, build, and productionize agentic AI systems—LLM-powered agents that can plan, use tools, orchestrate workflows, and operate reliably under enterprise constraints. You will own key parts of the agent architecture (planning, tool use, memory, evaluation, safety/guardrails, and observability) and deliver end-to-end solutions across RAG, function/tool calling, multi-agent coordination, and scalable deployment.

Key Responsibilities

• Design and implement agentic systems: single-agent and multi-agent architectures (planner/executor, supervisor-worker, routing, reflection, critique, task decomposition).
• Build robust tool-using agents: function calling, tool schemas, tool authorization, retries, rate limiting, and sandboxing.
• Implement RAG + memory patterns: retrieval strategies, hybrid search, context assembly, long-term memory, and grounding/citation behaviors.
• Develop workflow orchestration for agent execution (state machines/graphs), concurrency controls, and deterministic execution where possible.
• Productionize GenAI services: APIs, background jobs, streaming responses, caching, and cost/latency optimization.
• Establish agent evaluation: golden sets, simulation-based evals, LLM-as-judge with mitigations, task success metrics, regression testing.
• Build observability and safety: tracing, token/tool telemetry, anomaly detection, prompt injection defenses, data leakage prevention, policy enforcement.
• Collaborate with product, security, and platform teams to deliver enterprise-ready solutions and integrate with internal systems (data, identity, workflow).
• Mentor engineers, set coding standards, and contribute to architecture reviews and technical roadmaps.

Required Qualifications

• 6+ years software engineering experience; 2+ years building LLM/GenAI systems in production.
• Strong programming skills in Python (required) and/or TypeScript/Node.js.
• Hands-on experience building agents (tool calling, planning, routing, multi-step workflows) beyond simple chatbots.
• Solid understanding of prompting, context window management, grounding, hallucination failure modes, and mitigation strategies.
• Experience with RAG: embeddings, vector databases, chunking strategies, hybrid retrieval, re-ranking.
• Proven ability to ship production services: Docker/Kubernetes, REST/gRPC, CI/CD, monitoring, incident response basics.
• Strong data/security instincts: secrets management, PII handling, secure tool access, least privilege