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 leads 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.
- Solid 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.

📌 Senior/ Manager Software Engineer (Bengaluru)
🏢 Talent21
📍 Bengaluru