Dl Sr cloud data engineer Jobs – 88 Open Jobs Today

Assistant Vice President
EXL Service —Delhi
- Full-time
- Computer Science
- System design
- Master's degree
15h
Software Developer
KVH Industries, Inc. —New Delhi District, Delhi
- Full-time
- Computer Science
- B.Sc
- Salesforce
Coding Specialist (Fluent in Marathi) - Freelance AI Trainer Project
Meridial —Delhi, Delhi
- ₹8 - ₹65 an hour
- Remote
- Doctoral degree
- Computer Science
- Master's degree
- Health insurance
- Paid time off
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AWL India —Delhi
- Part-time
- Azure
- Computer Science
- Software troubleshooting
Coding Specialist (Fluent in Telugu) - Freelance AI Trainer Project
Meridial —Delhi, Delhi
- ₹8 - ₹65 an hour
- Remote
- Doctoral degree
- Computer Science
- Master's degree
- Health insurance
- Paid time off
Senior Sales Engineer
SatSure Analytics India —Unknown, Delhi
- Computer Science
- Project management
- Bachelor's degree
- Health insurance
- Paid time off
Senior Backend Platform Engineer, AI Enablement (Rails)
Cialfo —Delhi, Delhi
- Azure
- Computer Science
- Master's degree
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Assistant Vice President

EXL Service -
Delhi

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Job details

Full-time
15 hours ago

Qualifications

CI/CD
Blackboard
Law
Computer Science
MCP
System design
Master's degree
OS Kernels
Quality assurance
Bachelor's degree
Continuous integration
APIs
Solution architecture
AI
Graph databases

Full job description

Job Description: Key Responsibilities

1. Agentic Solution Architecture & Design Authority

Lead discovery and solutioning with stakeholders; translate business objectives into target-state agentic AI architectures, blueprints, and roadmaps .
Own end-to-end solution design : multi-agent orchestration, tool-using agents, human-in-the-loop patterns, memory and state management, RAG and knowledge layers, and enterprise integration.
Drive build vs. buy vs. partner decisions for models, agent frameworks, and solution with EXL standards.

2. Architecture Standards, Governance & Responsible AI

Define and enforce reference architectures, design standards, and reusable patterns for agentic AI solutions across accounts.
Embed security, privacy, compliance, and responsible AI – including agent guardrails, evaluation frameworks, and auditability – into every design.
Conduct architecture and design reviews , ensuring solutions are scalable, cost-efficient, and production-grade.

3. Technical Leadership Through Delivery

Guide Forward Deployment Engineers, data scientists, and delivery teams from design through production – remaining hands-on at critical points (prototyping, integration, performance tuning).
De-risk delivery by resolving complex technical blockers : legacy integration, agent reliability, model performance, and latency/cost/quality trade-offs.
Ensure solutions move beyond POCs to enterprise-wide adoption and value realization .

4. Stakeholder Engagement & Advisory

Act as trusted technical advisor to CIOs, CDOs and enterprise architects; lead architecture workshops, design authority boards, and executive briefings.
Support pre-sales and strategic deals : solution shaping, effort estimation, technical proposals, and orals.
Articulate architecture decisions in business terms – value, risk, cost, and time-to-market.

5. Capability Building & Reuse

Convert engagement learnings into reusable assets, accelerators, and reference implementations for EXL’s agentic AI portfolio.
Mentor architects and senior engineers; raise the architecture bar across the Enterprise AI practice.
Continuously track and translate emerging AI advances (Agentic AI, LLMs, autonomous systems) into EXL-ready architecture strategies.

Technical & Architecture Expertise (Agentic AI)

Multi-agent system design : supervisor–worker hierarchies, planner–executor and reflection loops, blackboard and swarm patterns; task decomposition, delegation, and inter-agent communication protocols; deciding when a single-agent vs. multi-agent topology is architecturally justified.
Agent state, memory & context engineering : short-term vs. episodic vs. semantic memory design, checkpointing and resumability, durable execution for long-running agents; context-window budgeting, compaction/summarization strategies, and retrieval-augmented context assembly.
Framework and protocol depth : LangGraph (graph state machines, interrupts, human-in-the-loop nodes), CrewAI, AutoGen/Semantic Kernel; MCP (Model Context Protocol) for tool and resource federation and A2A for agent interoperability; sound judgment on custom orchestration vs. framework adoption.
Model strategy & token economics : model portfolio design and routing (frontier LLMs vs. SLMs), structured outputs and function-calling schema design, constrained decoding; fine-tuning vs. RAG vs. prompt-optimization trade-offs; prompt caching, batching, distillation, and quantization to hit latency and cost SLOs.
Retrieval & knowledge architecture : hybrid retrieval (sparse + dense), rerankers, GraphRAG and knowledge graphs; chunking and embedding strategy, freshness pipelines, and access-control-aware retrieval (document/row-level security) for regulated enterprises.
Evaluation architecture : golden datasets, LLM-as-judge with calibration, trajectory-level agent evals, regression harnesses wired into CI/CD gates, and online canary/A-B evaluation for continuous quality assurance.
Guardrails, safety & governance : prompt-injection and jailbreak defenses, PII detection/redaction, policy engines, sandboxed tool execution, human-approval gates for high-risk actions, and full audit trails/lineage for responsible AI and regulatory compliance.
Production & platform architecture : model gateways, multi-tenancy, VPC/private endpoints, HA/DR, autoscaling, rate limiting, and circuit breakers; observability via distributed tracing (OpenTelemetry), token/cost telemetry, and drift monitoring at enterprise scale.
Enterprise integration : event-driven and API-led integration patterns, identity propagation (OAuth/OIDC), secrets management, and integrating agents with CRM, contact center, workflow platforms, and legacy estates.
Multimodal & emerging stacks : voice agents (streaming ASR/TTS – e.g., ElevenLabs), avatar/video (HeyGen), computer-use agents; fluency with AI-native tooling (Claude Code, Cursor) and evolving OpenAI/Anthropic platform capabilities.

Responsibilities: Robust, scalable agentic architectures that move engagements from POC to enterprise-wide production adoption.
Reference architectures, patterns, and accelerators reused across multiple accounts – reducing time-to-value and delivery risk.
Tangible business outcomes (productivity, cost, quality, revenue) enabled by sound architecture decisions.
Strong security, compliance, and responsible AI posture across all designed solutions.

Recognized technical credibility with CTO/CIO organizations, contributing to account growth and strategic deal wins.

Qualifications: 12+ years of experience in software/solution architecture, data, or digital transformation, with 3+ years architecting AI/LLM or agentic AI solutions .
Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
Proven track record of:
- Architecting and delivering production AI/GenAI solutions for large enterprise clients
- Serving as design authority across multiple concurrent engagements or programs
- Operating in business-facing, consulting, or forward-deployed environments with senior stakeholders
Strong understanding of:
- Agentic AI and LLM architectures , RAG, evaluation, and guardrails
- Data platforms, cloud, security, and compliance
- Enterprise integration and legacy modernization
Experience engaging with CTOs, CIOs, enterprise architects, and executive stakeholders .

Willingness to travel and work onsite at business locations as required.

Apply Now

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sr cloud data engineer jobs in dl

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Job Description: Key Responsibilities

Technical & Architecture Expertise (Agentic AI)

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