Custom Software Engineer

Custom Software Engineer

Project Role: Custom Software Engineer

Project Role Description: Develop custom software solutions to design, code, and enhance components across systems or applications. Use modern frameworks and agile practices to deliver scalable, high-performing solutions tailored to specific business needs.

Must Have Skills: Machine Learning

Good To Have Skills: NA

Minimum 7.5 year(s) of experience is required

Educational Qualification: 15 years full time education

Summary: As a Software Development Engineer, you will engage in a dynamic work environment where you will analyze, design, code, and test various components of application code across multiple clients. Your typical day will involve collaborating with team members to ensure the successful implementation of software solutions, performing maintenance and enhancements, and contributing to the overall development process. You will be responsible for delivering high-quality code while adhering to best practices and project timelines, ensuring that the applications meet the needs of the clients effectively.

Roles & Responsibilities

• Seasoned IT software delivery professional with an experience of 10+ years development and application architecture.
• Leadership & Collaboration skills
• Strategic technical leadership and vision. Sets technical direction for multiple teams or an entire platform, defining architecture and quality standards.
• Expert in the complete TDLC, continuously improving methodologies and tooling.
• Guides the adoption of new technologies (e.g. cloud services, automation frameworks) to enhance productivity.
• AI Governance Program: define policy controls, risk tiers, approval workflows, model & prompt change advisory board, and vendor due diligence; own DPAs & retention standards at solution level.
• Cost & Risk Guardrails: multi-provider routing policies (cost/latency/safety); monthly unit-economics reviews (cost-per-resolution, ROI).
• Enterprise Protocols: champion MCPs & standard AI comms protocols for tool/knowledge connectors; mandate schema contracts & audits.
• Resilience & Portability: disaster-recovery for embeddings & indexes; cross-cloud portability for LLM endpoints; fallbacks & degrade-gracefully patterns.
• Outcome-first Roadmapping: set value hypotheses and measurement plans per use case (deflection, CSAT, time-to-market).

• Broad and authoritative domain mastery across distribution channels.
• Strategy with business leaders using deep knowledge of mutual fund distribution, brokerage, retirement products, etc.
• Shapes technical solutions to align with financial domain trends and future regulatory changes.
• Drives domain-oriented innovation – e.g. suggesting platform capabilities to support new investment offerings or compliance mandates.
• Ensures engineering teams maintain a strong focus on domain needs to achieve highly regulated objectives while delivering customer value.

• Drives AI-enabled engineering at scale.
• Highly AI-fluent and adept at leveraging AI throughout the development lifecycle. Uses advanced features (customizing AI models or scripts) to automate repetitive coding, code reviews, or data analysis. Coaches team members on prompt engineering and best practices for AI-assisted development.
• Evaluates and introduces new AI tools to the team (for instance, AI-based testing or DevOps assistants) to improve efficiency. Understands generative AI capabilities enough to integrate AI-driven components into products when appropriate. Helps define team strategy as AI agents offload more dev tasks.
• Strong working experience in Generative AI application development using open source or closed source foundation models.
• Champions AI adoption across the organization, ensuring teams have the tools and skills to capitalize on AI's benefits. Defines policies for safe, effective use of AI (balancing productivity with code quality and data security). Identifies strategic opportunities to use AI/ML in products and internal processes – e.g. using AI to modernize legacy code or improve customer personalization. By 2027, with AI-native development rising, leads efforts to reskill engineers. Ensures that human creativity and expertise complement AI automation for optimal outcomes.

Professional & Technical Skills

• Must To Have Skills: Proficiency in Machine Learning.
• Strong understanding of various machine learning algorithms and their applications.
• Experience with data preprocessing and feature engineering techniques.
• Familiarity with programming languages such as Python or R for implementing machine learning models.
• Ability to work with large datasets and perform data analysis to derive insights.

Additional Information: The candidate should have minimum 7.5 years of experience in Machine Learning. This position is based at our Gurugram office. A 15 years full time education is required.