Najafgarh Delhi Delhi Delivery management leader – ai productivity solutions Jobs – 111 Open Jobs Today

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AI & Automation Lead / Manager

AIS InfoSource
Gurugram, Haryana

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Job details

Qualifications

CI/CD
Azure
Law
Computer Science
MCP
Kubernetes
Enterprise Software
C#
Encryption
Google Cloud Platform
.NET
Master's degree
AWS
Docker
Presentation skills
Bachelor's degree
Continuous integration
REST
APIs
AI
Banking
Python
Analytics

Full job description

AI & Automation Lead / Manager

EXPERIENCE REQUIRED: 6+ Years

NUMBER OF POSITIONS: 01

DEPARTMENT: Information Systems

REPORTS TO: CIO / Head of Product & AI Strategy

LOCATION: Gurugram, India

ROLE OVERVIEW:

AIS is hiring for an AI & Automation Lead/Manager to own AI product delivery end-to-end — architecture, security, and production scale — across this platform, serving banks, auto lenders, mortgage/home lenders, and telecom clients. This is a hands-on architect-plus-engineering-manager role. You’ll make the architecture and security calls yourself, lead the engineers who build against them, and be the technical voice on AI to the company’s senior leaders. You own turning an AI idea into a governed, production-grade system — not just the prototype.

ESSENTIAL DUTIES AND RESPONSIBILITIES:

1. AI Architecture & Technical Strateg

—

Own architecture decisions for AI features on AIS’s platform: LLM/model selection, RAG and agentic design, vector store and retrieval strategy, MCP-based integration

—

Set reusable design standards — prompt/context management, evaluation, guardrails — so features aren’t built as one-offs

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Make build-vs-buy and vendor calls (OpenAI, Anthropic, Azure OpenAI, automation platforms) on cost, latency, accuracy, and lock-in

2. Zero-to-One Delivery & Scaling

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Take AI ideas from prototype to a production system that holds up under real case volumes and audit scrutiny

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Define AIS’s bar for “production-ready”: accuracy thresholds, latency budgets, human-in-the-loop fallback, monitoring, rollback plans

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Own the path from single-workflow pilot to full-population, multi-client rollout, including the data pipeline and infra work scale requires

3. Security, Compliance & Governance

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Own security and privacy posture for every AI system touching client data, consumer PII, and case information – access controls, encryption, vendor data-handling terms, retention

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Build and enforce an AI governance framework: model risk classification, human-oversight checkpoints, audit logging, explainability

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Partner with compliance, legal, and security (internal and client-side) so AI features clear regulatory bar before shipping

4. Engineering Leadership

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Hire, mentor, and set the technical bar for a team of AI/GenAI engineers and automation developers; review architecture and code at key checkpoints

—

Turn ambiguous business asks into scoped workstreams with clear ownership and definition of done; run delivery across concurrent initiatives

5. Stakeholder Communication

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Be the primary technical voice on AI to the senior leaders — explain trade-offs, risk, and ROI in terms an executive audience can act on

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Collaborate and defend roadmap materials (capability-to-lifecycle mapping, Now/Next/Later views); support client/RFP conversations where AI is a differentiator

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Push back on scope or timeline when the technical reality doesn’t match the ask

6. MLOps & Production Operations

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Own monitoring, drift tracking, incident response, and continuous evaluation for every deployed AI feature

—

Track the metrics that matter per feature (accuracy, cost per transaction, latency, escalation rate) and drive iteration on them

SUPERVISORY RESPONSIBILITIES:

—

Provide technical guidance and mentorship

—

Lead related projects and coordinate with cross-functional teams

—

Establish process speficic standards, procedures, and best practices

SKILLS:

—

6+ years in software/AI engineering, including 4+ years hands-on GenAI/LLM or applied ML, with proven ownership of architecture — not just implementation — for production AI systems

—

Track record taking at least one AI/automation product from concept to production at meaningful scale

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Hands-on with LLM integration (OpenAI, Anthropic/Claude, Azure OpenAI), RAG and vector databases, and agentic/orchestration frameworks (LangChain, LangGraph or similar), including MCP-based integration

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Experience owning security, data governance, or compliance posture for AI systems in a regulated or sensitive-data environment (financial services, healthcare, insurance strongly preferred)

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Demonstrated people leadership — has hired, mentored, and directly managed engineers, not just coordinated a project team

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Direct experience presenting to senior/executive stakeholders — roadmaps, architecture decisions, or vendor evaluations at that level

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Prior experience in fintech, lending, banking operations & servicing, insurance, or another regulated financial-services domain

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Experience building or governing an AI Center of Excellence, model risk framework, or Responsible AI program

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Experience automating compliance-adjacent workflows (call/quality monitoring, document review, claims processing) via integration with enterprise systems

QUALIFICATIONS:

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Bachelor’s or Master’s in Computer Science, AI/ML, Data Engineering, or related field.

LANGUAGE SKILLS:

Ability to read, analyze and interpret the most complex documents. Ability to respond effectively to the most sensitive inquiries or complaints. Ability to write emails, speeches and articles using original or innovative techniques or style. Ability to make effective and persuasive speeches and presentations on controversial or complex topics to top management, public groups and clients.

REASONING ABILITY:

Ability to define problems, collect data, establish facts, and draw valid conclusions. Ability to interpret an extensive variety of technical instructions in mathematical or diagram form and deal with several abstract and concrete variables.

CERTIFICATES, LICENSES, REGISTRATION:

—

Relevant certification (Google Cloud, Microsoft Azure AI, or equivalent)

—

Strong fundamentals: Python and/or C#/.NET, REST APIs, cloud (Azure/AWS/GCP), Docker/Kubernetes, CI/CD

WORK ENVIRONMENT:

The work environment characteristics described here are representative of those an employee encounters while performing the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.

While performing the duties of this job, the noise level in the work environment is usually quiet to moderate.

DISCLAIMER:

The information in this job description is designed to indicate the general nature and level of work performed by employees within this classification. It is not designed to contain or be interpreted as a comprehensive inventory of all duties, responsibilities and qualifications required of employees assigned to this position and may be changed at the company’s discretion to conform to business needs.

ABOUT THE COMPANY:

AIS is a Texas-based fintech firm committed to lowering operating costs, improving quality and reducing cycle time with back-office automation, highly skilled talent and standardized reporting and analytics solutions. AIS manages the day-to-day work so our clients can focus on growing their business. We review client processes, eliminate non-value adds, and enhance productivity. We build financial and legal technology to automate and optimize workforce performance. We recruit, train, and manage specialized human resources to meet staff augmentation needs. We equip decision makers with deep data sets and forward-thinking analytics so they can make smarter business decisions and create better customer experiences. We serve a variety of industries including banking, automotive finance, credit card, mortgage, insurance and telecommunications.

Apply Now

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I want to receive the latest job alerts for delivery management leader – ai productivity solutions in najafgarh delhi delhi

Explore jobs in more locations

AI & Automation Lead / Manager

1. AI Architecture & Technical Strateg

2. Zero-to-One Delivery & Scaling

3. Security, Compliance & Governance

4. Engineering Leadership

5. Stakeholder Communication

6. MLOps & Production Operations

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