Senior AI Engineer

Location: Mumbai, Maharashtra, India

Sector: Technology, Information & Media

Company Size: 500 - 1,000 Employees

Employment: Full-Time, Permanent

Experience: 10 - 14 Years (Engineering Leadership)

Level: Engineering Manager / Group EM

ABOUT THIS MANDATE :

Recruiting Bond has been exclusively retained by one of India's most prominent and well-established digital platform organisations operating at the intersection of Technology, Information, and Media to identify and place an exceptional Engineering Manager who can lead engineering teams through an enterprise-wide AI adoption and digital transformation agenda.

This is a high-impact, hands-on leadership role at the nexus of people, product, and technology. The organisation is executing one of the most ambitious AI transformation programmes in its sector and this Engineering Manager will be a core driver of that change. You will lead multiple squads, own engineering delivery end-to-end, embed AI tooling and practices into the team's DNA, and shape the engineering culture of tomorrow.

We are seeking leaders who code when it matters, who build systems and teams with equal conviction, and who view AI not as a trend but as a fundamental shift in how great software is built.

THE OPPORTUNITY AT A GLANCE :

AI-First Engineering Culture :

• Own AI adoption across your squads - from LLM tooling integration to automation-first delivery workflows. Make AI a default, not an afterthought.

Hands-On Engineering Leadership :

• Stay close to the code. Lead architecture reviews, unblock engineers, and set the technical bar - not just the management agenda.

People & Org Builder :

• Grow engineers into leaders. Build squads of 615 across functions. Drive hiring, career frameworks, and a culture of psychological safety.

KEY RESPONSIBILITIES :

1. Hands-On Technical Engagement :

• Remain deeply embedded in the technical work participate in design reviews, architecture decisions, and critical code reviews
• Set and uphold the engineering quality bar : performance benchmarks, security standards, test coverage, and release quality
• Provide technical direction on backend platform strategy, API design, service decomposition, and data architecture
• Identify and resolve systemic technical debt and architectural risks across team-owned services
• Unblock engineers by diving into complex problems debugging, pair programming, and system analysis when it matters
• Own key technical decisions in collaboration with Tech Leads and Principal Engineers; balance pragmatism with long-term sustainability

2. AI Adoption, Integration & Transformation (2026 Mandate) :

• Define and execute the team's AI adoption roadmap - from developer tooling to product-facing AI features
• Champion the integration of GenAI tools (GitHub Copilot, Cursor, Claude, ChatGPT) across the full engineering workflow coding, testing, documentation, incident response
• Embed LLM-powered capabilities into the product : recommendation engines, intelligent search, conversational interfaces, content generation, and predictive systems
• Lead evaluation and adoption of AI-assisted SDLC practices : automated code review, AI-generated test suites, intelligent observability, and anomaly detection
• Partner with Data Science and ML Platform teams to productionise ML models with robust MLOps pipelines
• Build team literacy in prompt engineering, RAG (Retrieval-Augmented Generation), and AI agent frameworks
• Create an experimentation culture : run structured AI pilots, measure productivity impact, and scale what works
• Stay ahead of the AI tooling landscape and advise senior leadership on strategic AI investments and engineering implications

3. People Leadership & Team Development :

• Lead, manage, and grow squads of 6 - 15 engineers across seniority levels (L2 through L6 / Junior through Staff)
• Conduct structured 1 : 1s, career growth conversations, and development planning with every direct report
• Design and execute personalised AI upskilling programmes ensure every engineer develops practical AI fluency by end of 2026
• Build and maintain a high-performance team culture : clarity of ownership, accountability, fast feedback loops, and psychological safety
• Drive performance management fairly and rigorously recognise top performers, manage underperformance constructively
• Lead technical hiring end-to-end : define job requirements, conduct bar-raising interviews, and make data-driven hire decisions
• Contribute to engineering career frameworks and level definitions in partnership with the VP / Director of Engineering

4. Engineering Delivery & Execution Excellence :

• Own end-to-end delivery for multiple product squads from planning and scoping through production release and post-launch stability
• Implement and refine agile delivery frameworks (Scrum, Kanban, Shape Up) calibrated to squad needs and product cadence
• Drive predictable delivery : maintain healthy sprint velocity, manage WIP limits, and ensure dependency resolution across teams.
• Establish and own engineering KPIs : DORA metrics (deployment frequency, lead time, MTTR, change failure rate), uptime SLOs, and velocity trends
• Lead incident management : build blameless post-mortem culture, own RCA processes, and drive systemic reliability improvements
• Balance technical debt repayment with feature velocity negotiate prioritisation transparently with Product leadership

5. Strategic Leadership & Cross-Functional Influence :

• Serve as the primary engineering partner for Product, Design, Data, and Business stakeholders translate ambiguity into executable engineering plans
• Participate in quarterly roadmap planning, capacity forecasting, and OKR definition for engineering teams
• Represent engineering in leadership forums articulate technical constraints, risks, and opportunities in business terms
• Contribute to org-wide engineering strategy : platform investments, build-vs-buy decisions, and shared infrastructure priorities
• Build relationships across geographies (Mumbai HQ + distributed teams) to maintain alignment and delivery cohesion
• Act as a culture carrier and ambassador for engineering excellence, innovation, and responsible AI use

AI TRANSFORMATION LEADERSHIP 2026 EXPECTATIONS :

In 2026, Engineering Managers at this organisation are expected to be active architects of AI transformation not passive observers. The following outlines the specific AI leadership expectations for this role :

AI Developer Productivity

• Drive measurable uplift in developer velocity through AI tooling adoption. Target : 30%+ reduction in code review cycle time and 40%+ increase in test coverage automation by Q3 2026.

LLM & GenAI Product Features

• Own delivery of GenAI-powered product capabilities : intelligent content, semantic search, personalisation, and conversational UX in production, at scale.

AI-Augmented Observability

• Implement AI-driven monitoring and anomaly detection pipelines. Reduce MTTR by leveraging predictive alerting, intelligent runbooks, and auto-remediation scripts.

Team AI Fluency :

• Build mandatory AI literacy across all engineering levels.
• Every engineer understands prompt engineering basics, AI ethics guardrails, and responsible AI deployment practices.

Responsible AI Governance :

• Partner with Security, Legal, and Data Privacy to ensure all AI deployments meet compliance standards, bias mitigation requirements, and explainability benchmarks.

TECHNOLOGY STACK & DOMAIN FAMILIARITY REQUIRED :

• Languages: Java/ Go/ Python/ Node.js /PHP /Rust (must be hands-on in at least 2)
• Cloud: AWS / GCP / Azure (multi-cloud exposure strongly preferred)
• AI & GenAI: OpenAI / Anthropic / Gemini APIs /LangChain /LlamaIndex / RAG / Vector DBs / GitHub
• Copilot: Cursor /Hugging Face
• Containers: Docker /Kubernetes /Helm /Service Mesh (Istio / Linkerd)
• Databases: PostgreSQL /MongoDB / Redis / Cassandra / Elasticsearch / Pinecone (Vector DB)
• Messaging: Apache Kafka /RabbitMQ /AWS SQS/SNS /Google Pub/Sub
• MLOps & DataOps: MLflow /Kubeflow / SageMaker / Vertex AI /Airflow /dbt
• Observability: Datadog /Prometheus /Grafana /OpenTelemetry / Jaeger /ELK Stack
• CI/CD & IaC: GitHub Actions ArgoCD / Jenkins / Terraform /Ansible /Backstage (IDP)

QUALIFICATIONS & CANDIDATE PROFILE :

Education :

• B.E. / B.Tech or M.E. / M.Tech from a Tier-I or Tier-II Institution - CS, IS, ECE, AI/ML streams strongly preferred
• Demonstrated engineering depth and leadership impact may complement institution pedigree

Experience :

• 10 to 14 years of progressive engineering experience, with at least 3 years in a formal Engineering Manager or equivalent people-leadership role
• Proven track record of managing and scaling engineering teams (615+ engineers) in a fast-growing SaaS or digital product environment
• Hands-on backend engineering background must be able to read, write, and critique production code
• Direct experience driving AI/ML feature delivery or AI tooling adoption within engineering organisations
• Exposure across start-up, mid-size, and large-scale product organisations, preferred adaptability is a core requirement
• Strong CS fundamentals: distributed systems, algorithms, system design, and software architecture
• Demonstrated career stability minimum of 2 years of average tenure per organisation.

The Ideal Engineering Manager in 2026 :

• Leads with context, not control, empowers engineers while maintaining accountability and quality
• Is fluent in both people language and technical language, switches registers naturally with engineers and executives alike
• Sees AI as a force multiplier for the team, not a threat. Actively experiments with and advocates for AI tooling
• Measures success by team outcomes, not personal output. Takes pride in what the team ships, not what they build alone
• Creates feedback loops obsessively between product and engineering, between seniors and juniors, between metrics and decisions
• Has strong opinions, loosely held, brings conviction to discussions but updates on evidence
• Invests in engineering excellence as seriously as delivery velocity knows that quality and speed are not opposites

WHY THIS ROLE STANDS APART :

AI Transformation at Scale :

• Lead one of the most significant AI adoption programmes in India's digital media sector.
• Our decisions will shape how hundreds of engineers work in 2026 and beyond.

Hands-On & Strategic Balance :

• A rare EM role that actively encourages technical depth.
• Stay close to the code while owning the people agenda - the best of both worlds.

Established Platform, Real Scale :

• 5001,000 engineers, proven product-market fit, and the org maturity to execute.
• This is not a greenfield startup gamble it is a serious company with serious ambition.

Clear Leadership Growth Path :

• A visible, direct path toward Director / VP of Engineering.
• Senior leadership is invested in growing its next generation of technology executives.

Job Title: AI Engineer

Location: Bengaluru 

Experience: 3 Years 

Working Days: 5 Days

About the Role

We’re reimagining how enterprises interact with documents and workflows—starting with BFSI and healthcare. Our AI-first platforms are transforming credit decisioning, document intelligence, and underwriting at scale. The focus is on Intelligent Document Processing (IDP), GenAI-powered analysis, and human-in-the-loop (HITL) automation to accelerate outcomes across lending, insurance, and compliance workflows.

As an AI Engineer, you’ll be part of a high-caliber engineering team building next-gen AI systems that:

• Power robust APIs and platforms used by underwriters, credit analysts, and financial institutions.
• Build and integrate GenAI agents.
• Enable “human-in-the-loop” workflows for high-assurance decisions in real-world conditions.

Key Responsibilities

• Build and optimize ML/DL models for document understanding, classification, and summarization.
• Apply LLMs and RAG techniques for validation, search, and question-answering tasks.
• Design and maintain data pipelines for structured and unstructured inputs (PDFs, OCR text, JSON, etc.).
• Package and deploy models as REST APIs or microservices in production environments.
• Collaborate with engineering teams to integrate models into existing products and workflows.
• Continuously monitor and retrain models to ensure reliability and performance.
• Stay updated on emerging AI frameworks, architectures, and open-source tools; propose improvements to internal systems.

Required Skills & Experience

• 2–5 years of hands-on experience in AI/ML model development, fine-tuning, and building ML solutions.
• Strong Python proficiency with libraries such as NumPy, Pandas, scikit-learn, PyTorch, or TensorFlow.
• Solid understanding of transformers, embeddings, and NLP pipelines.
• Experience working with LLMs (OpenAI, Claude, Gemini, etc.) and frameworks like LangChain.
• Exposure to OCR, document parsing, and unstructured text analytics.
• Familiarity with model serving, APIs, and microservice architectures (FastAPI, Flask).
• Working knowledge of Docker, cloud environments (AWS/GCP/Azure), and CI/CD pipelines.
• Strong grasp of data preprocessing, evaluation metrics, and model validation workflows.
• Excellent problem-solving ability, structured thinking, and clean, production-ready coding practices.

Job Overview

Architect and build scalable, high-performance backend systems while working on mission-critical platforms that process real-time market data and portfolio analytics. The role also involves leveraging Generative AI capabilities to enhance data intelligence, automation, and user-facing features, while ensuring regulatory compliance and secure financial transactions.

Key Responsibilities

• Design, develop, and maintain scalable backend services and APIs using NodeJS and Python
• Build event-driven architectures using RabbitMQ and Kafka for real-time data processing
• Develop and manage data pipelines integrating PostgreSQL and BigQuery for analytics and warehousing
• Integrate and deploy Generative AI models (LLMs, embeddings, AI APIs) into backend systems for automation, insights, and intelligent workflows
• Design AI-powered features such as recommendation systems, document processing, or conversational interfaces
• Ensure system reliability, security, and low-latency performance for mission-critical systems
• Lead technical design discussions, conduct code reviews, and mentor junior engineers
• Optimize database queries, implement caching strategies, and improve overall system performance
• Collaborate with cross-functional teams to deliver end-to-end product features
• Implement monitoring, logging, and observability solutions

Required Skills and Qualifications

• 2+ years of professional backend development experience
• Strong expertise in NodeJS and Python for production-grade applications
• Proven experience building RESTful APIs and microservices architectures
• Experience working with Generative AI frameworks/APIs (OpenAI, LangChain, vector databases, prompt engineering)
• Understanding of integrating LLMs into production systems (RAG, embeddings, fine-tuning basics)
• Strong proficiency in PostgreSQL, including query optimization and schema design
• Hands-on experience with RabbitMQ and Kafka
• Experience with BigQuery or similar data warehousing solutions
• Solid understanding of distributed systems, scalability patterns, and high-traffic applications
• Strong knowledge of authentication, authorization, and security best practices
• Experience with Git, CI/CD pipelines, and modern development workflows
• Excellent problem-solving and debugging skills
• Exposure to fintech or financial services, cloud platforms (GCP/AWS/Azure), Docker/Kubernetes, caching tools (Redis/Memcached), and regulatory requirements (KYC, compliance, data privacy) is a plus

Apply directly at: https://wohlig.keka.com/careers/jobdetails/136351

Experience: 8+ years, senior candidates only | Type: Full-time | Location: Remote (India)

 ---

 WHAT WE'RE BUILDING

 See http://www.juliet.space

 We're building Juliet, an AI that runs marketing end to end. Our users are marketers, founders, CEOs, growth leads, agencies, and SMBs — not developers. They

 tell Juliet the goal. She plans, writes production code, and ships real marketing: conversion-optimized websites, launch assets, campaigns, audits, autonomously.

 That's the engineering problem in one line: the humans in the loop can't read code, so the agent has to get it right on her own — plan, build, self-correct,

 recover, ship.

 Under the hood: a browser-based studio backed by cloud sandboxes, a real-time SSE streaming pipeline, and a LangGraph agent working across 83 tools and 63 skill

 modules. The agent isn't bolted onto the product. She is the product.

 Small team, big ambitions. You'll ship things users touch daily, not write tickets about them.

 ---

 THE ROLE

 We're hiring one architect-level backend engineer to own Juliet's agentic infrastructure end to end. That means the agent graph, the execution environment, the

 streaming pipeline, the state and memory systems — and setting technical direction for the engineers working alongside you.

 This is a player-coach seat. You'll still write code every day, and your architectural calls become the product. You'll work directly with the founder. No PMs in

 between.

 Frontend is part of the system. You won't be leading it, but you'll need to understand how the agent's output reaches the browser and be able to ship full-stack

 features when needed.

 ---

 THE STACK

 AI agent (primary): Python 3.11, LangGraph 1.x + LangChain, Anthropic / Google / OpenAI model providers

 API (primary): NestJS 11, Supabase, Redis, PostgreSQL, Server-Sent Events

 Infra (primary): Modal cloud sandboxes, Docker, Netlify deployments

 Frontend (secondary): Next.js 15, React 19, TypeScript, Zustand, CodeMirror 6, XTerm.js

 Monorepo: Turborepo, pnpm

 ---

 WHAT YOU'LL WORK ON

 The majority of your time is here:

 Agentic AI workflows — Design, extend, and harden the LangGraph agent graph: multi-step planning, code generation, tool dispatch, self-correction, and recovery

 across 83 tools and 63 skill modules. This is the core of the product.

 Real-time streaming architecture — The SSE pipeline that carries every agent action from the Python backend through NestJS to the browser: event framing,

 reconnection, health monitoring, interrupt handling for plan approvals and clarifying questions.

 Agent execution environments — Sandbox lifecycle on Modal: container spin-up, file sync, terminal I/O, command execution, and live preview with per-asset esbuild

 bundling. The agent lives here.

 State and memory systems — LangGraph Postgres checkpointers, middleware-injected context (goals, design docs, memory anchors), conversation summarization. How

 the agent knows what it knows.

 Backend API and data layer — NestJS services, Supabase schema, Redis caching, quota enforcement, webhook handling. The plumbing the agent depends on.

 Marketing intelligence pipelines — AEO, CRO, and brand-perception audit engines: multi-LLM probing, parallel inference, streamed structured reports, result

 caching. Audit-at-scale infrastructure.

 The remaining ~25% of your time:

 Full-stack product features — Collaboration (roles and permissions), the Netlify deployment pipeline, subscription and quota flows, onboarding. You'll ship these

 end to end — backend first, frontend to close the loop.

 ---

 WHAT WE'RE LOOKING FOR

 Must-have:

 - 8+ years of professional software engineering, including meaningful time as a tech lead or systems architect who owned something end to end. Closer to ten is

 the norm for people who thrive here.

 - Both worlds on your resume: engineering rigor inside a large company and 0-to-1 ownership at an early-stage startup.

 - Production agentic systems experience. You've built and operated LLM agent systems in production with LangGraph, LangChain, or equivalent — agent graphs, tool

 use, state management, prompt engineering, evals. This means well beyond calling a chat endpoint.

 - Strong Python. You design and ship production Python daily. The agent codebase is yours to own.

 - Architect-level system design. You can own how data flows across four services, make tradeoffs under uncertainty, and defend every call.

 - AI-native development workflow. You drive Claude Code, Codex, or similar agentic tools as everyday instruments — not occasionally. You have opinions about

 working with coding agents because you do it constantly.

 - Real-time backend systems. You've built SSE, WebSocket, or streaming API infrastructure in production — not just consumed it.

 - Strong TypeScript. The API layer and most product features are in TypeScript. You're productive in it.

 Strong plus:

 - Background in developer tools, IDEs, or coding/execution platforms

 - Container runtimes and sandboxed execution (Modal, E2B, Firecracker, or similar)

 - Depth in PostgreSQL, Redis, and Supabase

 - LLM observability and evals tooling (LangSmith or similar)

 - NestJS or equivalent Node.js API framework experience

 - React/Next.js — enough to ship a full-stack feature without handoff

 - Exposure to marketing, growth, or publisher-facing products

 ---

 WHY THIS ROLE IS DIFFERENT

 You own the architecture. Not a feature factory. Not someone else's design doc. The technical execution of an AI product is yours to lead.

 The agent is the product. You're not adding AI to an existing system. You're building and operating the system that is the AI. Every architectural decision

 touches what Juliet can and can't do.

 Hard problems, always. The system spans cloud sandboxes, streaming infrastructure, multi-step agent graphs, and a full-stack web product — for non-technical

 users who can't course-correct a broken output. The bar is high.

 Small team, real leverage. Your code ships to users the same week. No layers of approval.

 ---

 HOW TO APPLY

 Send us:

 1. A short note on the most complex agentic system you've shipped: what broke, and what you'd redo. A link to something you've built that involves agent graphs, tool use, or autonomous multi-step execution

 2. What is one thing you would improve about Juliet? It could be a feature or a bug.

About the company

The client is building an AI-native platform for modern law firms.

Legal work is evolving rapidly, but much of the industry's workflow continues to rely on fragmented software and manual processes. The team is building an intelligent platform that helps law firms streamline work across the entire legal lifecycle, including document screening, drafting, filing, research, compliance, and other high-value legal workflows.

Their founding team combines deep expertise across both law and technology, giving us a unique perspective on how AI can fundamentally reshape legal operations while maintaining the precision and reliability that the profession demands.

We're looking for engineers who want to build products from first principles, move quickly, and help define the future of AI in legal technology.

About the role

We're hiring a Founding Software Engineer to help build the core platform from the ground up.

This is a high-ownership role. You'll work across backend systems, AI infrastructure, product architecture, and deployment, collaborating directly with the founders to design and ship features that reach customers quickly.

If you enjoy solving hard engineering problems, shipping fast, and working in a small, ambitious team, we'd like to talk.

What You'll Build

• AI-powered legal workflows for law firms
• Agentic systems for drafting, screening, and legal document analysis
• Reliable backend services and APIs
• Retrieval and knowledge systems for legal intelligence
• Evaluation pipelines to improve AI quality and reliability
• Internal developer tooling and scalable platform infrastructure

What We're Looking For

• 3-5 years of software engineering experience
• Strong Proficiency in at least one backend language: Python (FastAPI or Django), or Node.js
• Experience working with at least one database: MongoDB, PostgreSQL, MySQL, or another relational database
• Understanding of modern AI application architecture, including concepts such as: Agentic systems AI SDKs Retrieval-Augmented Generation (RAG) Prompt engineering Evaluation frameworks (Evals)
• Strong system design and problem-solving skills
• Ability to ship production-quality software quickly
• High ownership and bias toward execution
• Comfortable learning new technologies as the product evolves

Nice to Have

• Experience building products in Compliance, FinTech, RegTech, or LegalTech
• Experience deploying and operating AI-powered production systems
• Familiarity with cloud platforms and modern deployment workflows
• Experience working in an early-stage startup

What We Value

• High agency
• Strong engineering fundamentals
• Curiosity and continuous learning
• Fast execution without compromising quality
• Clear communication and collaborative problem solving

Why Join Us

• You'll be joining at the earliest stage of the company and will help shape both the product and the engineering culture.
• This is an opportunity to work directly with founders who combine legal and technical expertise, solve meaningful problems for the legal industry, and build AI systems that are used every day by legal professionals.
• If building from zero excites you more than maintaining legacy systems, we'd love to hear from you

7+ years of experience in Python Development

Good experience in Microservices and APIs development.

Must have exposure to large scale data

Good to have Gen AI experience

Code versioning and collaboration. (Git)

Knowledge for Libraries for extracting data from websites.

Knowledge of SQL and NoSQL databases

Familiarity with RESTful APIs

Familiarity with Cloud (Azure /AWS) technologies

About Wissen Technology:

• The Wissen Group was founded in the year 2000. Wissen Technology, a part of Wissen Group, was established in the year 2015.

• Wissen Technology is a specialized technology company that delivers high-end consulting for organizations in the Banking & Finance, Telecom, and Healthcare domains. We help clients build world class products.

• Our workforce consists of 550+ highly skilled professionals, with leadership and senior management executives who have graduated from Ivy League Universities like Wharton, MIT, IITs, IIMs, and NITs and with rich work experience in some of the biggest companies in the world.

• Wissen Technology has grown its revenues by 400% in these five years without any external funding or investments.

• Globally present with offices US, India, UK, Australia, Mexico, and Canada.

• We offer an array of services including Application Development, Artificial Intelligence & Machine Learning, Big Data & Analytics, Visualization & Business Intelligence, Robotic Process Automation, Cloud, Mobility, Agile & DevOps, Quality Assurance & Test Automation.

• Wissen Technology has been certified as a Great Place to Work®.

• Wissen Technology has been voted as the Top 20 AI/ML vendor by CIO Insider in 2020.

• Over the years, Wissen Group has successfully delivered $650 million worth of projects for more than 20 of the Fortune 500 companies.

We have served client across sectors like Banking, Telecom, Healthcare, Manufacturing, and Energy. They include likes of Morgan Stanley, MSCI, StateStreet, Flipkart, Swiggy, Trafigura, GE to name a few.

Website : www.wissen.com

Key Responsibilities

Platform Build & Architecture

• Refactor an existing Python-based prototype into a modular, production-grade platform

• Define clear service boundaries (API layer, orchestration, agent runtime, data access)

• Build reusable components that allow extension without exposing core engine logic Agent Framework & Orchestration

• Design and implement frameworks for AI agents and reporting workflows

• Build orchestration for multi-step execution (deterministic + AI-driven)

• Ensure outputs are traceable, auditable, and suitable for financial reporting Developer Enablement

• Enable internal/client developers to: o build and deploy reporting agents o reuse approved components o access platform capabilities via APIs (without direct code access)

• Implement access controls and abstraction layers Full Stack Development

• Lead development across: o Backend: Python, FastAPI o Frontend: Next.js o Real-time: WebSockets

• Build simple internal interfaces for: o job execution o monitoring o output review Code Governance & DevOps

Own development workflows in Azure DevOps • branching strategy, PRs, code reviews, merges • Set up CI/CD pipelines, environments (dev/test/prod), and release processes • Ensure code quality, testing, and maintainability standards Team Leadership (Near-term) • Act as the technical anchor o shore • Mentor and guide future hires as the team scales • Establish best practices across code, documentation, and delivery

Required Skills

• Strong experience in Python backend development (FastAPI or similar)

• Experience with React / Next.js

• Familiarity with WebSockets or real-time systems

• Experience building APIs and scalable backend systems

• Hands-on experience with Azure DevOps (repos, pipelines, PR workflows)

• Understanding of modular architecture, access control, and system design • Ability to operate in an early-stage, fast-evolving environment

About Corridor Platforms

Corridor Platforms is a leader in next-generation risk decisioning and responsible AI governance, empowering banks and lenders to build transparent, compliant, and data-driven solutions. Our platforms combine advanced analytics, real-time data integration, and GenAI to support complex financial decision workflows for regulated industries.

Role Overview

As a Backend Engineer at Corridor Platforms, you will:

• Architect, develop, and maintain backend components for our Risk Decisioning Platform.
• Build and orchestrate scalable backend services that automate, optimize, and monitor high-value credit and risk decisions in real time.
• Integrate with ORM layers – such as SQLAlchemy – and multi RDBMS solutions (Postgres, MySQL, Oracle, MSSQL, etc) to ensure data integrity, scalability, and compliance.
• Collaborate closely with Product Team, Data Scientists, QA Teams to create extensible APIs, workflow automation, and AI governance features.
• Architect workflows for privacy, auditability, versioned traceability, and role-based access control, ensuring adherence to regulatory frameworks.
• Take ownership from requirements to deployment, seeing your code deliver real impact in the lives of customers and end users.

Technical Skills

• Languages: Python 3.9+, SQL, JavaScript/TypeScript, Angular
• Frameworks: Flask, SQLAlchemy, Celery, Marshmallow, Apache Spark
• Databases: PostgreSQL, Oracle, SQL Server, Redis
• Tools: pytest, Docker, Git, Nx
• Cloud: Experience with AWS, Azure, or GCP preferred
• Monitoring: Familiarity with OpenTelemetry and logging frameworks

Why Join Us?

• Cutting-Edge Tech: Work hands-on with the latest AI, cloud-native workflows, and big data tools—all within a single compliant platform.
• End-to-End Impact: Contribute to mission-critical backend systems, from core data models to live production decision services.
• Innovation at Scale: Engineer solutions that process vast data volumes, helping financial institutions innovate safely and effectively.
• Mission-Driven: Join a passionate team advancing fair, transparent, and compliant risk decisioning at the forefront of fintech and AI governance.

What We’re Looking For

• Proficiency in Python, SQLAlchemy (or similar ORM), and SQL databases.
• Experience developing and maintaining scalable backend services, including API, data orchestration, ML workflows,  and workflow automation.
• Solid understanding of data modeling, distributed systems, and backend architecture for regulated environments.
• Curiosity and drive to work at the intersection of AI/ML, fintech, and regulatory technology.
• Experience mentoring and guiding junior developers.

Ready to build backends that shape the future of decision intelligence and responsible AI?

Apply now and become part of the innovation at Corridor Platforms!

Company Overview 

McKinley Rice is not just a company; it's a dynamic community, the next evolutionary step in professional development. Spiritually, we're a hub where individuals and companies converge to unleash their full potential. Organizationally, we are a conglomerate composed of various entities, each contributing to the larger narrative of global excellence.

Redrob by McKinley Rice: Redefining Prospecting in the Modern Sales Era

Backed by a $40 million Series A funding from leading Korean & US VCs, Redrob is building the next frontier in global outbound sales. We’re not just another database—we’re a platform designed to eliminate the chaos of traditional prospecting. In a world where sales leaders chase meetings and deals through outdated CRMs, fragmented tools, and costly lead-gen platforms, Redrob provides a unified solution that brings everything under one roof.

Inspired by the breakthroughs of Salesforce, LinkedIn, and HubSpot, we’re creating a future where anyone, not just enterprise giants, can access real-time, high-quality data on 700 M+ decision-makers, all in just a few clicks.

At Redrob, we believe the way businesses find and engage prospects is broken. Sales teams deserve better than recycled data, clunky workflows, and opaque credit-based systems. That’s why we’ve built a seamless engine for:

• Precision prospecting
• Intent-based targeting
• Data enrichment from 16+ premium sources
• AI-driven workflows to book more meetings, faster

We’re not just streamlining outbound—we’re making it smarter, scalable, and accessible. Whether you’re an ambitious startup or a scaled SaaS company, Redrob is your growth copilot for unlocking warm conversations with the right people, globally.

EXPERIENCE

Duties you'll be entrusted with:

• Develop and execute scalable APIs and applications using the Node.js or Nest.js framework
• Writing efficient, reusable, testable, and scalable code.
• Understanding, analyzing, and implementing – Business needs, feature modification requests, and conversion into software components
• Integration of user-oriented elements into different applications, data storage solutions
• Developing – Backend components to enhance performance and receptiveness, server-side logic, and platform, statistical learning models, highly responsive web applications
• Designing and implementing – High availability and low latency applications, data protection and security features
• Performance tuning and automation of applications and enhancing the functionalities of current software systems.
• Keeping abreast with the latest technology and trends.

Expectations from you:

Basic Requirements

• Minimum qualification: Bachelor’s degree or more in Computer Science, Software Engineering, Artificial Intelligence, or a related field.
• Experience with Cloud platforms (AWS, Azure, GCP).
• Strong understanding of monitoring, logging, and observability practices.
• Experience with event-driven architectures (e.g., Kafka, RabbitMQ).
• Expertise in designing, implementing, and optimizing Elasticsearch.
• Work with modern tools including Jira, Slack, GitHub, Google Docs, etc.
• Expertise in Event driven architecture.
• Experience in Integrating Generative AI APIs.
• Working experience in high user concurrency.
• Experience in scaled databases for handling millions of records - indexing, retrieval, etc.,

Technical Skills

• Demonstrable experience in web application development with expertise in Node.js or Nest.js.
• Knowledge of database technologies and agile development methodologies.
• Experience working with databases, such as MySQL or MongoDB.
• Familiarity with web development frameworks, such as Express.js.
• Understanding of microservices architecture and DevOps principles.
• Well-versed with AWS and serverless architecture.

Soft Skills

• A quick and critical thinker with the ability to come up with a number of ideas about a topic and bring fresh and innovative ideas to the table to enhance the visual impact of our content.
• Potential to apply innovative and exciting ideas, concepts, and technologies.
• Stay up-to-date with the latest design trends, animation techniques, and software advancements.
• Multi-tasking and time-management skills, with the ability to prioritize tasks.

THRIVE

Some of the extensive benefits of being part of our team:

• We offer skill enhancement and educational reimbursement opportunities to help you further develop your expertise.
• The Member Reward Program provides an opportunity for you to earn up to INR 85,000 as an annual Performance Bonus.
• The McKinley Cares Program has a wide range of benefits:
• The wellness program covers sessions for mental wellness, and fitness and offers health insurance.
• In-house benefits have a referral bonus window and sponsored social functions.
• An Expanded Leave Basket including paid Maternity and Paternity Leaves and rejuvenation Leaves apart from the regular 20 leaves per annum. 
• Our Family Support benefits not only include maternity and paternity leaves but also extend to provide childcare benefits.
• In addition to the retention bonus, our McKinley Retention Benefits program also includes a Leave Travel Allowance program.
• We also offer an exclusive McKinley Loan Program designed to assist our employees during challenging times and alleviate financial burdens.

Must have:

• 8+ years of experience with a significant focus on developing, deploying & supporting AI solutions in production environments.
• Proven experience in building enterprise software products for B2B businesses, particularly in the supply chain domain.
• Good understanding of Generics, OOPs concepts & Design Patterns
• Solid engineering and coding skills. Ability to write high-performance production quality code in Python
• Proficiency with ML libraries and frameworks (e.g., Pandas, TensorFlow, PyTorch, scikit-learn).
• Strong expertise in time series forecasting using stat, ML, DL and foundation models
• Experience of working on processing time series data employing techniques such as decomposition, clustering, outlier detection & treatment
• Exposure to generative AI models and agent architectures on platforms such as AWS Bedrock, Crew AI, Mosaic/Databricks, Azure
• Experience of working with modern data architectures, including data lakes and data warehouses, having leveraged one or more of the frameworks such as Airbyte, Airflow, Dagster, AWS Glue, Snowflake,, DBT
• Hands-on experience with cloud platforms (e.g., AWS, Azure, GCP) and deploying ML models in cloud environments.
• Excellent problem-solving skills and the ability to work independently as well as in a collaborative team environment.
• Effective communication skills, with the ability to convey complex technical concepts to non-technical stakeholders

Good To Have:

• Experience with MLOps tools and practices for continuous integration and deployment of ML models.
• Has familiarity with deploying applications on Kubernetes
• Knowledge of supply chain management principles and challenges.
• A Master's or Ph.D. in Computer Science, Machine Learning, Data Science, or a related field is preferred