Senior/ Manager Software Engineer

Talent21 Management and Shared Services Pvt Ltd; Bengaluru; Vor Ort; INR 4.200.000 - 7.000.000; Vollzeit

Zusammenfassung

Talent21 Management and Shared Services Pvt Ltd in Bengaluru seeks a senior Engineering Leader to drive AI-enabled strategies and oversee multiple software programs. You will balance business priorities, client commitments, and technical governance while promoting responsible AI adoption across engineering teams.

You will lead planning, estimation, and production readiness, align capacity and priorities across teams, and mentor engineering managers to foster a high‑performance culture focused on

Qualifikationen

• Bachelor's degree or equivalent experience in CS or engineering.
• 13+ years of software engineering experience with multi-team leadership.
• Experience delivering enterprise-scale software programs using Agile practices.
• Ability to balance client commitments, strategy, delivery and people development.
• Strong leadership, communication, and stakeholder management skills.

Aufgaben

• Lead end-to-end delivery of multiple strategic software programs across teams.
• Partner with clients, product leaders and architects to shape scalable solutions.
• Define AI adoption roadmap across engineering, quality, and support.
• Drive engineering excellence through governance, reviews and debt management.
• Provide architectural direction for enterprise web, API and data solutions.
• Coach engineering managers and build a high-performance culture.

Kenntnisse

AI strategy

Leadership

C#/.NET

REST APIs

React/Angular

JavaScript/TypeScript

SQL/PLSQL

Git

LangChain/LangGraph

AI frameworks

Ausbildung

Bachelor's degree in Computer Science or related field

Tools

Git

LangChain

LangGraph

Amazon Bedrock

Azure AI

Snowflake

Redshift

SnapLogic

DataHub

n8n

Jobbeschreibung

Responsibilities

You will lead engineering outcomes across multiple teams and initiatives, balancing business priorities, client commitments, technical strategy, quality, talent development, and innovation. You will promote AI-assisted ways of working and enable leaders and engineers to operate effectively across traditional role boundaries.

Delivery & Portfolio Leadership

• Own end-to-end delivery of multiple strategic software programs, ensuring predictable execution, measurable outcomes, quality, transparency, and timely management of risks and dependencies.
• Lead planning, estimation, engineering, testing, implementation, transition, and production stabilization across build, enhancement, modernization, migration, and maintenance initiatives.
• Align capacity, skills, priorities, milestones, and dependencies across teams; make informed trade-offs and call out material risks with clear mitigation options.
• Establish outcome-focused governance using delivery health, quality, productivity, customer impact, and operational measures rather than activity alone.
• Ensure alignment to engineering standards, security, privacy, compliance, accessibility, documentation, and support-readiness expectations.
• Promote continuous improvement through retrospectives, root-cause analysis, reusable practices, automation, and evidence-based decision-making.

Client & Stakeholder Partnership

• Partner with clients, product leaders, business collaborators, architects, analysts, quality leaders, data teams, and operations teams to understand objectives and shape scalable solutions.
• Lead customer workshops, solution discussions, technical presentations, roadmap conversations, and executive governance forums.
• Build trusted relationships, establish clear expectations, manage issues constructively, and communicate decisions, trade-offs, risks, and mitigation plans with transparency.
• Translate business objectives into technology strategies, investment priorities, capability roadmaps, and measurable engineering outcomes.
• Communicate delivery health, key performance indicators, solution risks, quality trends, innovation progress, and strategic recommendations to senior partners.

AI Strategy, Engineering & Innovation

• Architect model and framework strategy, RAG/knowledge architecture, embeddings/vector search, prompt/context management, tool integration, agent orchestration, memory, guardrails, evaluation and human oversight.
• Define and implement an AI adoption roadmap across engineering, analysis, quality, support, knowledge management, and customer-solution use cases.
• Identify high-value Generative AI and Agentic AI opportunities, prioritize use cases, sponsor prototypes, define success measures, and scale proven patterns responsibly.
• Promote effective use of frameworks and services such as LangChain, LangGraph, Amazon Bedrock, Azure AI, Amazon Q, Kiro, Microsoft Copilot, or equivalent technologies.
• Apply responsible AI practices covering privacy, security, intellectual property, data handling, model limitations, accuracy, bias awareness, explainability, traceability, and accountable human oversight.
• Create an environment that supports responsible experimentation, reusable accelerators, knowledge sharing, and measurable improvement in engineering productivity and customer value.

Technical Strategy & Engineering Leadership

• Provide architectural direction and technical guidance across enterprise web, API, integration, distributed, data, and AI-enabled solutions.
• Guide conversion of high-level designs into detailed designs covering components, APIs, data models, algorithms, schemas, integration contracts, security, resilience, performance, and non-functional requirements.
• Partner with architects and technical leaders to evaluate solution options and ensure appropriate scalability, security, maintainability, observability, accessibility, and cost effectiveness.
• Drive engineering excellence through design reviews, code reviews, architecture governance, quality standards, technical-debt management, performance analysis, and root-cause prevention.
• Maintain sufficient hands‑on technical awareness to challenge assumptions, assess feasibility, guide complex decisions, and support resolution of critical engineering issues.
• Promote modern engineering patterns for C#/.NET, Python, JavaScript/TypeScript, React or Angular, APIs, event‑driven systems, distributed applications, and data‑intensive workloads.

Quality Engineering & Technical Assurance

• Own the quality strategy across teams, including testability, automation, code quality, data quality, performance, resilience, security validation, and production readiness.
• Ensure quality is built into discovery, design, development, and support rather than treated as a separate downstream activity.
• Establish appropriate review mechanisms, test coverage expectations, traceability, defect‑prevention practices, release validations, and operational acceptance criteria.
• Use trends from defects, incidents, customer feedback, and engineering measures to direct corrective and preventive improvements.
• Promote AI‑assisted test design, review, defect analysis, and knowledge discovery while requiring accountable validation of generated outputs.

Data, Platforms & Solution Integration

• Guide scalable data modelling, integration, batch and real‑time processing, data‑quality controls, and performance strategies across enterprise solutions.
• Provide oversight for solutions using Oracle, MySQL, DynamoDB, Redshift, Snowflake, or equivalent data platforms.
• Guide designs involving REST APIs, event‑driven patterns, microservices, integration platforms, and technologies such as SnapLogic, DataHub, or equivalents.
• Ensure data solutions address reliability, lineage, security, privacy, interoperability, and business usability.

People Leadership & Organizational Capability

• Lead, coach, and mentor engineering managers, technical leads, and engineers across multiple teams.
• Build a high‑performance culture grounded in accountability, inclusion, collaboration, engineering excellence, continuous learning, and customer focus.
• Drive workforce planning, hiring, onboarding, goal alignment, regular feedback, career development, succession planning, recognition, retention, and employee engagement.
• Develop future‑ready capabilities across software engineering, architecture, quality, data, AI, automation, client engagement, and domain knowledge.
• Create clear ownership and decision mechanisms while empowering leaders and teams to implement effectively.
• Address performance and team‑health concerns fairly, consistently, transparently, and in alignment with organizational policies.

Flexibility, Automation & Continuous Improvement

• Enable teams to flex across business analysis, architecture, development, quality engineering, data, automation, documentation, and production support based on delivery priorities while preserving role depth and accountability.
• Use workflow automation and low‑code tools such as n8n, Make, Zapier, or equivalent technologies to streamline repeatable engineering and business processes where appropriate.
• Encourage cross‑skilling, communities of practice, reusable assets, structured knowledge management, and movement across adjacent responsibilities.
• Continuously improve engineering practices, operating models, collaboration, customer responsiveness, and delivery efficiency using measurable evidence.

Qualifications

Essential Education & Experience

• Bachelor's degree or equivalent experience in Computer Science, Engineering, Information Technology, or a related field, or equivalent practical experience.
• 13+ years of software engineering experience, including 5+ years leading multiple engineering teams in customer-facing environments.
• Demonstrated success delivering complex, enterprise‑scale software programs and leading multi‑functional teams using Agile delivery practices.
• Experience managing engineering leaders and teams while balancing client commitments, technical strategy, delivery outcomes, quality, and people development.
• Strong leadership, communication, presentation, stakeholder management, decision‑making, organizational, prioritization, and problem‑solving skills.

Core Technical & Leadership Skills

• Strong technical understanding of C#, .NET/.NET Core, REST APIs, microservices, distributed systems, object‑oriented design, design patterns, and modern web architecture.
• Working knowledge of React or Angular, JavaScript/TypeScript, HTML5, CSS, and responsive application design.
• Strong understanding of SQL, PL/SQL, relational and NoSQL data modelling, database performance, data integration, batch and real‑time processing, and enterprise data platforms.
• Ability to guide architecture, engineering standards, application security, performance, observability, quality engineering, automated testing, troubleshooting, and production support.
• Experience with Git‑based collaboration, peer review, Agile engineering, structured release management, and enterprise change governance.
• Ability to connect technology choices to business outcomes, customer experience, delivery risk, talent capability, and long‑term maintainability.

AI & Future Skills

• Strong AI architecture capability across RAG, agentic systems, model evaluation, guardrails, identity, data protection, observability and cost/performance trade‑offs.
• Demonstrated architecture ownership for a meaningful AI solution from discovery through implementation, evaluation and operational readiness.
• Exposure to LangChain, LangGraph, CrewAI, Amazon Bedrock, Azure AI, or comparable AI frameworks and services.
• Ability to define AI strategy, prioritize use cases, guide prototypes, assess feasibility and risk, establish adoption measures, and scale successful practices.
• Ability to promote AI‑assisted ways of working across analysis, architecture, development, testing, documentation, knowledge management, and support with appropriate controls.

Desirable Skills & Experience

• Experience leading engineering for CRM, loyalty, marketing technology, customer data, high‑volume transaction, or large‑scale distributed platforms.
• Experience with data and integration technologies such as Redshift, Snowflake, DynamoDB, SnapLogic, DataHub, or equivalent platforms.
• Experience using Python, scripting, workflow automation, n8n, low‑code/no‑code platforms, or equivalent technologies.
• Experience establishing engineering communities, reusable accelerators, knowledge‑management practices, or enterprise modernization programs.
• Experience evaluating emerging technologies and translating successful experiments into governed, repeatable delivery capabilities.

Personal Attributes

• Strong customer focus, business insight, critical thinking, and an ownership mapproach
• Excellent leadership, coaching, mentoring, communication, presentation, negotiation, and stakeholder‑management capability.
• Collaborative, adaptable, resilient, decisive, and results oriented while remaining open to challenge and new evidence.
• Passion for innovation, continuous improvement, inclusive leadership, talent development, and measurable business impact.
• Professionalism, integrity, sound judgement, transparency, and accountability in decisions affecting customers, people, and technology.