Hyderabad Telangana Sr. engineering manager ai Jobs – 651 Open Jobs Today

Senior Development Manager - AI Engineering
PepsiCo —Hyderabad, Telangana
- Remote
- Master's degree
- Bachelor's degree
- APIs
AI Engineering Manager
Protolabs —Hyderabad, Telangana
- Full-time
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Charles Schwab —Hyderabad, Telangana
- Microsoft Access
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Sr. AI Product Manager
Cyara —Hyderabad, Telangana
- Hybrid work
- Master's degree
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Senior Engineering Manager
Cyara —Hyderabad, Telangana
- Hybrid work
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- Master's degree
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1d
Industry Consulting Sr. Manager
NTT Ltd —Hyderabad, Telangana
- Oracle
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Lead Technical Manager – Enterprise Planning, Data Architecture & AI Enablement
Mattel —Hyderabad, Telangana
- ₹32,54,068.80 - ₹40,67,586.00 a year
- Full-time
- Oracle
- Computer Science
- Salesforce
Quick apply
9d
Senior Manager – Information Technology (TIS)
DESRI —Hyderabad, Telangana
- Azure
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Sr Manager, Software Development & Engineering Senior
Charles Schwab —Hyderabad, Telangana
- Azure
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12d
Senior Manager, Systems Engineering Senior
Charles Schwab —Hyderabad, Telangana
- Computer Science
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- Bachelor's degree
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14d
Engineering Manager
Keka Technologies Pvt. Ltd —Hyderabad, Telangana
- Management
- Master's degree
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Global Technical Operations Manager
SEI Investments —Hyderabad, Telangana
- Full-time
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- Analysis skills
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Technical Product Manager
Wexa AI —Hyderabad, Telangana
- ₹25,00,000 - ₹30,00,000 a year
- Full-time
- Computer Science
- Data analysis skills
- Analysis skills
Quick apply
AI Engineering Manager
EPAM Systems, Inc. —Hyderabad, Telangana
- Azure
- Doctoral degree
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- Health insurance
- Paid time off
Sr Manager, Software Development & Engineering Lead (PL)
Charles Schwab —Hyderabad, Telangana
- Management
- Salesforce
- APIs
- Commuter assistance
- Health insurance
9d
Principal Software Engineering Manager
Microsoft —Hyderabad, Telangana
- Full-time
- Azure
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14h
Senior Manager Software Engineering
Optum —Hyderabad, Telangana
- Full-time
- Azure
- Computer Science
- Java
- Health insurance
6d
Senior Manager AI Strategies Technical Implementation
Cubic Corporation —Hyderabad, Telangana
- Full-time
- Azure
- AWS
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17h

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Senior Development Manager - AI Engineering

PepsiCo -
Hyderabad, Telangana

Apply Now

Job details

Qualifications

CI/CD
Backbone.js
Law
MCP
Enterprise Software
Master's degree
Bachelor's degree
Continuous integration
APIs
Data science
AI
Python

Full job description

Overview:

As an AI Engineer specializing in Agentic AI enablement, you will design, prototype, iterate, and productionize reusable agent capabilities that run on the enterprise AI Backbone across cloud and edge environments. You will build and harden agent behaviors (tool-use, policy constraints, memory/RAG patterns where applicable), create evaluation and regression test harnesses, and integrate agents with enterprise systems using MCP-style connectors/clients. You serve as the technical complement to the AI Solutions Lead by translating domain workflows into reliable agent components, while partnering closely with platform teams to deploy using standardized CI/CD, security, and observability patterns.

Responsibilities:

1) Agent Engineering & Productionization (40%)

Prototype, iterate, and productionize domain-aligned agent modules (plans, tool-use, task execution flows) that operate reliably within defined workflows. (Execute)
Build and maintain versioned agent assets (prompts, policies, tool schemas, configs) with clear change logs and reproducibility. (Execute)
Optimize agent performance for latency and token efficiency within defined constraints (especially for edge-targeted scenarios when applicable). (Execute)

2) Evaluation, Testing & Quality Signals (25%)

Implement an AI system testing harness for assigned agents: regression suites, golden test sets (where applicable), and comparison reports for prompt/model variants. (Execute)
Maintain evaluation metadata (test versioning, metrics, correlation IDs) to support traceability and repeatability. (Execute)
Contribute to safety/quality checks (hallucination, toxicity, policy compliance) as part of evaluation workflows defined by the program. (Execute/Consult)

3) Integration with Tools and MCPs (20%)

Implement or extend MCP clients/connectors for internal data products and approved enterprise apps using standardized interfaces, scopes, and audit patterns. (Execute)
Validate integration behaviour with sandbox credentials, representative test data, and end-to-end workflow tests with stakeholders. (Execute/Consult)

4) Operational Readiness & Collaboration (15%)

Ensure owned components meet operational readiness expectations: logging/telemetry coverage, runbook notes, basic SLI/SLO alignment for agent health and integration reliability. (Execute/Consult)
Collaborate with platform and transformation teams to clarify requirements, triage issues, and incorporate feedback from internal/external teams into improvements. (Execute/Consult/Informed)
Identify and implement small process improvements that increase repeatability (evaluation templates, prompt versioning conventions, integration test scaffolds). (Execute)

Decision-Making Autonomy: Moderate — owns technical implementation for assigned agents/evals/integrations within established patterns; escalates cross-domain/security/policy decisions.
Supervision Required: Moderate — receives design review and direction from L09/L10 AI leads for evaluation approach, routing standards, and sensitive integrations.
Complexity of Role: High (for L08) — requires balancing quality/latency, integrating multiple enterprise tools, and ensuring reproducible evaluation under evolving requirements.
Cross-Functional Interactions: Yes — frequent interactions with platform, product/domain, security, SRE/observability, and enterprise app owners.

Qualifications:

Key Skills/Experience Required Minimum Qualifications:

Minimum Qualifications

Bachelor’s/Master’s in CS/AI/ML/Data Science (or equivalent experience).
Hands-on experience building LLM applications (agents/tool-use/prompting) and shipping production code in Python.

Required Expertise

Python engineering with production hygiene (testing, packaging, structured logging)
Agentic AI frameworks/patterns: LangGraph/LangChain, CrewAI-style orchestration patterns; tool/function calling; prompt versioning
Evaluation discipline: test sets, regression testing, offline eval metrics, A/B comparisons, failure taxonomy
Integration engineering: APIs, auth concepts, schema-based tool integration; MCP-style interface implementation preferred
Observability basics: correlation IDs, error analysis, latency instrumentation
Cloud familiarity: enough to deploy and validate agents via platform pipelines (not owning infra)

Differentiating Competencies

Ownership: takes components from prototype tested

production-ready with clear artifacts
Process improvement mindset: improves repeatability and reduces rework through templates and automation
Collaboration & customer focus: works effectively with domain teams; builds what improves real workflows
Adaptability: adjusts quickly to changing model/tool constraints and evolving requirements
Communication: concise technical updates; can explain agent behaviour and evaluation results to non-experts

Apply Now

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