Enterprise Architect (Data & AI)

Accellor

  • Hyderabad, Telangana
  • Permanent
  • Full-time
  • 1 month ago
  • Apply easily
About US: -Headquartered in the Silicon Valley with offices in London, Hyderabad and Singapore, Accellor is a Microsoft Gold Partner and a premier Salesforce Partner that uses best-of-breed Cloud technology to deliver superior customer engagement and business effectiveness for clients. We bring a deep understanding of Financial, Retail, High Tech, Healthcare, and Retail industries, rolling out end-to-end implementation of salesforce.com and powerful third-party apps. We also build products that are sold on the AppExchange and used by both boutique businesses and Fortune 500 companies.We are about 250 strong with a majority of our team members based at Hyderabad, delivering best of breed cloud solutions to customers in the US, UK and APAC region. We’ve created an atmosphere that encourages curiosity, constant learning, and persistence. We encourage our employees to grow and explore their interests. We cultivate an environment of collaboration, autonomy and delegation – we know our people have a strong work ethic and a sense of pride and ownership over their work. They are passionate, eager, and motivated – focused on building the perfect solution but never losing sight of the bigger picture.Role Expectation: -As an Enterprise Architect, you will own the end-to-end technology blueprint, spanning backend platforms (Java/.NET, Python), frontend frameworks (React, Angular, Node.js), real-time data streaming, and AI-driven/agentic services. You will translate business objectives into an actionable, multi-year technology and AI roadmap; ensure that every layer (application, data, infrastructure, security, AI, agentic agents) is aligned and future-proof; and act as the bridge between C-suite strategy, product, sales engineering (presales), and delivery teams.Key Deliverables & Success Metrics * Architecture & AI Roadmap
  • Deliver a three-year, multi-domain blueprint covering cloud, data, integration, AI/ML, and agentic-AI agents
  • Stand up an AI & Agentic Architecture Council (quarterly) driving adoption of generative AI, conversational agents, and MLOps standards
  • AI-First Proof-of-Concepts & Agentic Demos
  • Lead 4–6 POCs/year around AI/ML and agentic use cases (e.g., LLM-powered assistants, workflow orchestration bots)
  • Measure POC success by model accuracy (+15% lift), inference latency (2× faster), and business KPIs (reduced support tickets, increased demo‐to‐close rate)
  • Team Enablement & AI Mentorship
  • Launch a monthly “AI & Agentic Deep Dive” series to upskill engineers, data scientists, and presales consultants on ML frameworks (TensorFlow, PyTorch), conversational-AI patterns, and agent orchestration
  • Embed AI/agentic design patterns into standard playbooks (prompt engineering, feedback loops, multi-agent coordination)
  • GTM & Presales Enablement
  • Collaborate with Sales Engineering to craft technical demos, solution blueprints, and ROI analyses for enterprise prospects
  • Support bid responses and RFPs with architecture diagrams, security/compliance narratives, and scalability proof points
  • Resilience & Responsible AI
  • Define and track system and model health metrics (system uptime ≥99.9%; model drift ≤5% per quarter)
  • Lead “AI fairness & ethics” reviews, ensuring bias detection, explainability, and compliance with GDPR/ADA
Expanded ResponsibilitiesA. Strategic Architecture & Agentic-AI Planning
  • Enterprise Blueprint: Evolve the canonical reference architecture to include AI/ML pipelines, feature stores, inference-at-the-edge, and autonomous agent orchestration
  • Cloud & Hybrid AI: Architect cloud-native AI/agentic services (SageMaker, Azure ML, Vertex AI Agents), hybrid inference runtimes, and GPU/TPU provisioning strategies
  • Standards & Policies: Author AI governance policies—data privacy, model validation, versioning, rollback strategies, and agent safety guardrails
B. Solution & AI-Driven Design
  • Core Platforms: Architect mission-critical microservices on Java/Spring Boot, .NET Core, and Python (Django, Flask, FastAPI) with embedded AI inference and agentic endpoints (REST/GRPC)
  • Frontend & Full-Stack: Design rich client applications using React, Angular, or Vue.js; backend APIs with Node.js/Express or Python frameworks; implement CI/CD for full-stack deployments
  • Data & Streaming: Design streaming ETL with Kafka + Spark/Flink feeding feature stores, real-time scoring engines, and agent event buses
  • MLOps & AI Ops: Define CI/CD for models (training, validation, deployment), automated retraining triggers, canary and shadow deployments, plus agent lifecycle management
C. Governance & Responsible AI
  • Architecture Reviews: Include an “ML & agentic risk” dimension in every design review (performance, security, bias, unintended behaviors)
  • Security & Compliance: Partner with InfoSec to secure code, model artifacts, and agent logic (encryption, access controls, audit trails); vet third-party AI/agentic services
  • FinOps for AI: Implement cost-optimization for GPU/compute, track ROI on AI and agentic initiatives (cost per model endpoint, agent-handling cost per transaction)
D. Leadership, GTM & Collaboration
  • Cross-Functional Engagement: Work closely with Product, UX, Sales Engineering, and Security to define AI/use-case roadmaps, demo strategies, and success criteria
  • Presales Coaching: Mentor Solutions Architects and Sales Engineers on technical storytelling, POC/demo best practices, and objection handling around AI and agentic capabilities
  • Community Building: Sponsor internal hackathons, open-source contributions (e.g., agent frameworks such as AutoGen, LangChain), and external speaking opportunities
E. AI & Agentic POC, Innovation, and GTM
  • Rapid Experimentation: Prototype generative AI agents, semantic search with vector databases, autonomous workflow bots, and conversational-AI pipelines
  • Benchmarking & Optimization: Lead performance profiling (JVM/CLR/Python interpreters), model quantization, optimization for CPU-only edge deployments, and low-latency agent responses
  • GTM Support: Develop presales playbooks, ROI calculators, and competitive battlecards for AI and agent-driven offerings
Mandatory Skills & Expertise
  • Languages & Frameworks:
  • Backend: Java (JEE, Spring Boot), .NET Core/Framework, Python (Django, Flask, FastAPI)
  • Frontend & Full-Stack: React, Angular, Vue.js, Node.js/Express, Next.js/Nuxt.js
  • APIs & Microservices: REST, gRPC, GraphQL, serverless functions (AWS Lambda, Azure Functions)
  • Streaming & Real-Time Data: Apache Kafka (Streams, Connect), Pulsar, Spark/Flink, event sourcing/CQRS
  • Cloud & AI Platforms: AWS (SageMaker, Lambda, ECS/EKS), Azure (ML, Functions, AKS), GCP (Vertex AI, Cloud Functions), Terraform, CloudFormation, Azure ARM
  • Containers & Orchestration: Docker, Kubernetes (EKS/AKS/GKE), Helm, service meshes (Istio, Linkerd)
  • Data Engineering & Feature Stores: Spark, Flink, Kinesis, S3/HDFS; data warehousing (Redshift, BigQuery, Snowflake); feature stores (Feast, Tecton)
  • AI/ML & Agentic Lifecycle: TensorFlow, PyTorch, MLflow, Kubeflow, SageMaker Pipelines; conversational-AI frameworks (Rasa, Bot Framework); agentic frameworks (LangChain, AutoGen)
  • Responsible AI & Ethics: Bias detection, explainability (SHAP, LIME), privacy-preserving ML (DP, federated learning), GDPR/PCI-DSS fundamentals
  • Distributed Systems & Performance: CAP theorem, consensus (Raft/Paxos), JVM/CLR/Python tuning, algorithmic complexity analysis, network diagnostics
  • GTM & Presales: Hands-on experience with technical presales, RFP/RFI responses, demo/PITCH deck creation, ROI analysis, competitive positioning
  • Leadership & Collaboration: Architecture governance, technical mentorship, stakeholder management, workshop facilitation, cross-functional team leadership.
Requirements
  • Bachelor’s or Master’s degree in Computer Science, Engineering, or related field
  • 15+ years delivering enterprise-grade solutions with significant AI/ML and agentic-AI components
  • Certifications (highly desirable): TOGAF 9.2, AWS Solutions Architect – Professional, Azure Solutions Architect Expert, Certified Kubernetes Administrator (CKA), TensorFlow Developer Certificate
Preferred Attributes:
  • Domain expertise in regulated industries (finance, healthcare, telecommunications)
  • Active open-source contributions to AI/agentic or frontend/backend frameworks
  • Proven track record driving agile transformations, DevSecOps, and responsible AI adoption at scale
BenefitsExciting Projects: We focus on industries like High-Tech, communication, media, healthcare, retail and telecom. Our customer list is full of fantastic global brands and leaders who love what we build for them.Collaborative Environment: You Can expand your skills by collaborating with a diverse team of highly talented people in an open, laidback environment — or even abroad in one of our global centres.Work-Life Balance: Accellor prioritizes work-life balance, which is why we offer flexible work schedules, opportunities to work from home, and paid time off and holidays.Professional Development: Our dedicated Learning & Development team regularly organizes Communication skills training, Stress Management program, professional certifications, and technical and soft skill trainings.Excellent Benefits: We provide our employees with competitive salaries, family medical insurance, Personal Accident Insurance, Periodic health awareness program, extended maternity leave, annual performance bonuses, and referral bonuses.

Accellor