Technical Architect - Machine Learning

Responsibilities Include:

● Architecting, building, testing, and deploying applications that support our transportation network

● Modifying designs and specifications of complex applications

● Collaborating and adding value through participation in peer code reviews, providing comments and suggestions

● Leverage AWS and other cloud technologies to design and implement systems in a serverless and event-driven environment

● Working with technical and non-technical end-users, other engineers, and product managers in a cross-functional, quick-moving, and collaborative environment

● Analyzing code, requirements, system risks, and software reliability and providing recommendations on how to leverage our technology more efficiently

● Working with Product and Design to confirm requirements, prioritization, and development of new features

● Design and implement LLM-powered features to solve complex logistics challenges, automate manual operational workflows, and unlock new efficiencies for our network.

● Champion AI-assisted engineering practices by integrating machine learning and intelligent automation into our CI/CD pipelines to predict deployment risks and accelerate delivery cycles.

What You Bring:

● Ability to adapt to changing requirements and work in a fast-paced environment.

● Ability to work effectively in a cross-functional team environment.

● 7+ years of experience building, testing and deploying applications in high-traffic production environments

● 7+ years of experience developing software with Javascript, Node.js and React

● Enjoys taking initiative, is a self-starter, willing to move fast and ship quick, and is excited about collaborating on big challenges

Preferred Qualifications:

● Experience in transportation, logistics or network orchestration systems is highly desirable

● Experience working in a remote first environment.

● Proficiency with AWS, GCP, or similar cloud platforms.

● Strong skills in React and Node.js for end-to-end development. 

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 Description – Machine Learning Expert

Role: Machine Learning Expert

Experience: 6+ Years

Location: Bangalore

Education: B.Tech Degree (Computer Science / Information Technology / Data Science / related fields)

Work Mode: Hybrid – 3 Days Office + 3 Days Work from Home

Interview Mode: Candidate must be willing to attend Face-to-Face (F2F) L2 round at Bangalore location

About the Role

We are seeking a highly skilled Machine Learning Expert with a strong background in building, training, and deploying AI/ML models. The ideal candidate will bring hands-on expertise in designing intelligent systems that leverage advanced algorithms, deep learning, and data-driven insights to solve complex business challenges.

Key Responsibilities

• Develop and implement machine learning and deep learning models for real-world business use cases.
• Perform data cleaning, preprocessing, feature engineering, and model optimization.
• Research, design, and apply state-of-the-art ML techniques across domains such as NLP, Computer Vision, or Predictive Analytics.
• Collaborate with data engineers and software developers to ensure seamless end-to-end ML solution deployment.
• Deploy ML models to production environments and monitor performance for scalability and accuracy.
• Stay updated with the latest advancements in Artificial Intelligence and Machine Learning frameworks.
• 
  

Required Skills & Qualifications

• B.Tech degree in Computer Science, Information Technology, Data Science, or related discipline.
• 6+ years of hands-on experience in Machine Learning, Artificial Intelligence, and Deep Learning.
• Strong expertise in Python and ML frameworks such as TensorFlow, PyTorch, Scikit-learn, Keras.
• Solid foundation in mathematics, statistics, algorithms, and probability.
• Experience in working with NLP, Computer Vision, Recommendation Systems, or Predictive Modeling.
• Knowledge of cloud platforms (AWS / GCP / Azure) for model deployment.
• Familiarity with MLOps tools and practices for lifecycle management.
• Excellent problem-solving skills and the ability to work in a collaborative environment.
• 
  

Preferred Skills (Good to Have)

• Experience with Big Data frameworks (Hadoop, Spark).
• Exposure to Generative AI, LLMs (Large Language Models), and advanced AI research.
• Contributions to open-source projects, publications, or patents in AI/ML.
• 
  

Work Mode & Interview Process

• Hybrid Model: 3 days in office (Bangalore) + 3 days remote.
• Interview: Candidate must be available for Face-to-Face L2 interview at Bangalore location.

The requirements are as follows:

1) Familiar with the the Django REST API Framework.

2) Experience with the FAST API framework will be a plus

3) Strong grasp of basic python programming concepts ( We do ask a lot of questions on this on our interviews :) )

4) Experience with databases like MongoDB , Postgres , Elasticsearch , REDIS will be a plus

5) Experience with any ML library will be a plus.

6) Familiarity with using git , writing unit test cases for all code written and CI/CD concepts will be a plus as well.

7) Familiar with basic code patterns like MVC.

8) Grasp on basic data structures.

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Should be highly Technical and hands-on experience in Artificial Intelligence and Machine learning and Python. Managing the successful delivery of projects by efficient planning and coordination.

KEY RESPONSIBILITIES OF THE POSITION :

• Create Technical Design for AI, Machine Learning, Deep Learning, NLP, NLU, NLG projects and implement the same in production.
• Solid understanding and experience of deep learning architectures and algorithms
• Working experience with AWS, most importantly AWS SageMaker, Aurora or MongoDB, Analytics and reporting.
• Experience solving problems in the industry using deep learning methods such as recurrent neural networks (RNN, LSTM), convolutional neural nets, auto-encoders, etc.
• Should have experience of 2-3 production implementations of machine learning projects.
• Knowledge of open-source libraries such as Keras, Tensor Flow, Pytorch
• Work with business analysts/consultants and other necessary teams to create a strong solution
• Should have in-depth understanding and experience of Data Science and Machine Learning projects using Python, R, etc. Skills in Java/C are a plus
• Should developing solutions using python in AI/ML projects
• Should be able to train and build a team of technical developers
• Desired to have experience as leads in designing and developing applications/tools using Microsoft technologies - ASP.Net, C#, HTML5, MVC
• Desired to have knowledge on any of the cloud solutions such as Azure or AWS
• Desired to have knowledge on any of container technology such as Docker
• Should be able to build strong relationships with project stakeholders

Keywords:

• Python
• Artificial Intelligence
• Machine Learning
• AWS
• Django
• NLP