4+ Data integration Jobs in Delhi, NCR and Gurgaon | Data integration Job openings in Delhi, NCR and Gurgaon

What We Are Looking For

CLOUDSUFI is seeking a senior, hands-on AI Platform Architect to design and build production-grade platforms for generative AI, agentic systems, data-intensive applications, and analytical workflows. This is a builder-architect role. The successful candidate will define architecture, make technology decisions, develop reference implementations, review critical code and designs, and guide engineering teams from prototypes to secure, scalable production systems. We are looking for a builder-architect with strong engineering judgement and practical delivery experience. The right candidate can define platform direction, evaluate trade-offs, validate ideas through implementation, and guide systems into production. They should be equally comfortable discussing distributed architecture, reviewing code, diagnosing workflow failures, designing evaluation systems, and mentoring engineering teams.

Key Responsibilities-

AI and Agentic Platform Architecture

• Design platforms for single-agent and multi-agent systems supporting planning, reasoning, tool use, memory, delegation, validation, and human approval.

• Define orchestration patterns for deterministic, dynamic, event-driven, and long-running AI workflows.

• Establish clear boundaries between LLM reasoning, application logic, quantitative computation, rules, and human decision-making.

• Evaluate and adopt agent frameworks, model providers, tools, and orchestration technologies based on reliability, flexibility, performance, and cost. Knowledge and Data Systems

• Architect RAG pipelines, document-processing systems, vector search, hybrid retrieval, knowledge graphs, and semantic data layers.

• Integrate structured and unstructured enterprise data from APIs, databases, files, streams, and external platforms.

• Design reusable workflows for research, data collection, transformation, analysis, modelling, validation, and reporting.

• Establish data lineage, provenance, metadata, access controls, freshness, and quality standards. Evaluation, Observability and Governance

• Build evaluation frameworks for accuracy, relevance, groundedness, task completion, tool use, safety, latency, and cost.

• Enable systematic experimentation across models, prompts, agents, tools, retrieval strategies, and orchestration patterns.

• Implement versioning and lifecycle management for prompts, agents, workflows, datasets, knowledge bases, evaluations, and model configurations.

• Establish tracing, monitoring, auditability, guardrails, approval workflows, and production quality diagnostics.

Cloud and Platform Engineering

• Define cloud-native architectures using microservices, APIs, event-driven systems, queues, schedulers, and distributed processing.

• Lead Kubernetes-based deployment, containerisation, CI/CD, Infrastructure as Code, environment management, and release automation.

• Design for horizontal scalability, fault tolerance, resilience, security, data privacy, and high availability.

• Optimise model usage, infrastructure, storage, retrieval, and compute for performance, latency, and cost.

Technical Leadership

• Translate product and business requirements into clear technical designs and implementation plans.

• Build prototypes and reference implementations for high-risk or foundational platform capabilities.

• Review architecture, code, interfaces, data models, infrastructure, and operational readiness.

• Define engineering standards and reusable patterns across AI, backend, data, and platform teams.

• Mentor senior engineers and support teams in resolving complex technical and production issues.

Required Skills and Experience

• 10+ years of experience in software architecture, platform engineering, distributed systems, data platforms, or AI systems.

• Strong hands-on experience designing and building production-grade AI or data-intensive platforms.

• Deep understanding of LLM applications, tool calling, structured outputs, RAG, embeddings, memory, and agent orchestration.

• Strong experience with cloud platforms, Kubernetes, containers, microservices, APIs, event driven architecture, CI/CD, and Infrastructure as Code.

• Experience with relational, document, graph, vector, and distributed data systems.

• Practical experience implementing AI evaluation, experimentation, tracing, monitoring, guardrails, and lifecycle management.

• Strong understanding of security, identity, access control, secrets management, data protection, and production reliability.

• Ability to move effectively between architecture, code, infrastructure, debugging, and technical delivery.

Good to Have

• Experience building enterprise AI copilots, autonomous workflows, research platforms, or analytical systems.

• Experience with knowledge graphs, hybrid search, model gateways, tool gateways, or agent marketplaces.

• Familiarity with LLMOps, MLOps, model serving, feature stores, model registries, and distributed compute.

• Experience supporting real-time and batch data processing at scale.

• Experience comparing and operating multiple commercial and open-source models.

• Prior experience in consulting, client-facing architecture, or complex enterprise platform delivery. 

MUST-HAVES:

• LLM, AI, Prompt Engineering LLM Integration & Prompt Engineering
• Context & Knowledge Base Design.
• Context & Knowledge Base Design.
• Experience running LLM evals

NOTICE PERIOD: Immediate – 30 Days

SKILLS: LLM, AI, PROMPT ENGINEERING

NICE TO HAVES:

Data Literacy & Modelling Awareness Familiarity with Databricks, AWS, and ChatGPT Environments

ROLE PROFICIENCY:

Role Scope / Deliverables:

• Scope of Role Serve as the link between business intelligence, data engineering, and AI application teams, ensuring the Large Language Model (LLM) interacts effectively with the modeled dataset.
• Define and curate the context and knowledge base that enables GPT to provide accurate, relevant, and compliant business insights.
• Collaborate with Data Analysts and System SMEs to identify, structure, and tag data elements that feed the LLM environment.
• Design, test, and refine prompt strategies and context frameworks that align GPT outputs with business objectives.
• Conduct evaluation and performance testing (evals) to validate LLM responses for accuracy, completeness, and relevance.
• Partner with IT and governance stakeholders to ensure secure, ethical, and controlled AI behavior within enterprise boundaries.

KEY DELIVERABLES:

• LLM Interaction Design Framework: Documentation of how GPT connects to the modeled dataset, including context injection, prompt templates, and retrieval logic.
• Knowledge Base Configuration: Curated and structured domain knowledge to enable precise and useful GPT responses (e.g., commercial definitions, data context, business rules).
• Evaluation Scripts & Test Results: Defined eval sets, scoring criteria, and output analysis to measure GPT accuracy and quality over time.
• Prompt Library & Usage Guidelines: Standardized prompts and design patterns to ensure consistent business interactions and outcomes.
• AI Performance Dashboard / Reporting: Visualizations or reports summarizing GPT response quality, usage trends, and continuous improvement metrics.
• Governance & Compliance Documentation: Inputs to data security, bias prevention, and responsible AI practices in collaboration with IT and compliance teams.

KEY SKILLS:

Technical & Analytical Skills:

• LLM Integration & Prompt Engineering – Understanding of how GPT models interact with structured and unstructured data to generate business-relevant insights.
• Context & Knowledge Base Design – Skilled in curating, structuring, and managing contextual data to optimize GPT accuracy and reliability.
• Evaluation & Testing Methods – Experience running LLM evals, defining scoring criteria, and assessing model quality across use cases.
• Data Literacy & Modeling Awareness – Familiar with relational and analytical data models to ensure alignment between data structures and AI responses.
• Familiarity with Databricks, AWS, and ChatGPT Environments – Capable of working in cloud-based analytics and AI environments for development, testing, and deployment.
• Scripting & Query Skills (e.g., SQL, Python) – Ability to extract, transform, and validate data for model training and evaluation workflows.
• Business & Collaboration Skills Cross-Functional Collaboration – Works effectively with business, data, and IT teams to align GPT capabilities with business objectives.
• Analytical Thinking & Problem Solving – Evaluates LLM outputs critically, identifies improvement opportunities, and translates findings into actionable refinements.
• Commercial Context Awareness – Understands how sales and marketing intelligence data should be represented and leveraged by GPT.
• Governance & Responsible AI Mindset – Applies enterprise AI standards for data security, privacy, and ethical use.
• Communication & Documentation – Clearly articulates AI logic, context structures, and testing results for both technical and non-technical audiences.

AWS Glue Developer 

Work Experience: 6 to 8 Years

Work Location:  Noida, Bangalore, Chennai & Hyderabad

Must Have Skills: AWS Glue, DMS, SQL, Python, PySpark, Data integrations and Data Ops, 

Job Reference ID:BT/F21/IND

Job Description:

Design, build and configure applications to meet business process and application requirements.

Responsibilities:

7 years of work experience with ETL, Data Modelling, and Data Architecture Proficient in ETL optimization, designing, coding, and tuning big data processes using Pyspark Extensive experience to build data platforms on AWS using core AWS services Step function, EMR, Lambda, Glue and Athena, Redshift, Postgres, RDS etc and design/develop data engineering solutions. Orchestrate using Airflow.

Technical Experience:

Hands-on experience on developing Data platform and its components Data Lake, cloud Datawarehouse, APIs, Batch and streaming data pipeline Experience with building data pipelines and applications to stream and process large datasets at low latencies.

➢ Enhancements, new development, defect resolution and production support of Big data ETL development using AWS native services.

➢ Create data pipeline architecture by designing and implementing data ingestion solutions.

➢ Integrate data sets using AWS services such as Glue, Lambda functions/ Airflow.

➢ Design and optimize data models on AWS Cloud using AWS data stores such as Redshift, RDS, S3, Athena.

➢ Author ETL processes using Python, Pyspark.

➢ Build Redshift Spectrum direct transformations and data modelling using data in S3.

➢ ETL process monitoring using CloudWatch events.

➢ You will be working in collaboration with other teams. Good communication must.

➢ Must have experience in using AWS services API, AWS CLI and SDK

Professional Attributes:

➢ Experience operating very large data warehouses or data lakes Expert-level skills in writing and optimizing SQL Extensive, real-world experience designing technology components for enterprise solutions and defining solution architectures and reference architectures with a focus on cloud technology.

➢ Must have 6+ years of big data ETL experience using Python, S3, Lambda, Dynamo DB, Athena, Glue in AWS environment.

➢ Expertise in S3, RDS, Redshift, Kinesis, EC2 clusters highly desired.

Qualification:

➢ Degree in Computer Science, Computer Engineering or equivalent.

Salary: Commensurate with experience and demonstrated competence

Do you want to work with the company which solves real time challenges by using Artificial Intelligence, Data Integration & Analytics, then read on.

Our client is an Ad Exchange startup redefining health care marketing.The startup has built an integrated marketplace for advertising to health practictioners by allowing them to access multiple digital health care platforms via single window. They have developed a patented technology that allows them to do precision targeting of ads for the physicians using programmatic media. They are soon to expand their services in the USA.

What you need to have: