Hiring Java Developers with Cloud

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.

The candidate should have extensive experience in designing and developing scalable data pipelines and real-time data processing solutions. As a key member of the team, the Senior Data Engineer will play a critical role in building end-to-end data workflows, supporting machine learning model deployment, and driving MLOps practices in a fast-paced, agile environment. Strong expertise in Apache Kafka, Apache Flink, AWS SageMaker, and Terraform is essential. Additional experience with infrastructure automation and CI/CD for ML models is a significant advantage.

Key Responsibilities

1. Design, develop, and maintain high-performance ETL and real-time data pipelines using Apache Kafka and Apache Flink.
2. Build scalable and automated MLOps pipelines for training, validation, and deployment of models using AWS SageMaker and associated services.
3. Implement and manage Infrastructure as Code (IaC) using Terraform to provision and manage AWS environments.
4. Collaborate with data scientists, ML engineers, and DevOps teams to streamline model deployment workflows and ensure reliable production delivery.
5. Optimize data storage and retrieval strategies for large-scale structured and unstructured datasets.
6. Develop data transformation logic and integrate data from various internal and external sources into data lakes and warehouses.
7. Monitor, troubleshoot, and enhance performance of data systems in a cloud-native, fast-evolving production setup.
8. Ensure adherence to data governance, privacy, and security standards across all data handling activities.
9. Document data engineering solutions and workflows to facilitate cross-functional understanding and ongoing maintenance.

Job Summary:

Deqode is looking for a highly motivated and experienced Python + AWS Developer to join our growing technology team. This role demands hands-on experience in backend development, cloud infrastructure (AWS), containerization, automation, and client communication. The ideal candidate should be a self-starter with a strong technical foundation and a passion for delivering high-quality, scalable solutions in a client-facing environment.

Key Responsibilities:

• Design, develop, and deploy backend services and APIs using Python.
• Build and maintain scalable infrastructure on AWS (EC2, S3, Lambda, RDS, etc.).
• Automate deployments and infrastructure with Terraform and Jenkins/GitHub Actions.
• Implement containerized environments using Docker and manage orchestration via Kubernetes.
• Write automation and scripting solutions in Bash/Shell to streamline operations.
• Work with relational databases like MySQL and SQL, including query optimization.
• Collaborate directly with clients to understand requirements and provide technical solutions.
• Ensure system reliability, performance, and scalability across environments.

Required Skills:

• 3.5+ years of hands-on experience in Python development.
• Strong expertise in AWS services such as EC2, Lambda, S3, RDS, IAM, CloudWatch.
• Good understanding of Terraform or other Infrastructure as Code tools.
• Proficient with Docker and container orchestration using Kubernetes.
• Experience with CI/CD tools like Jenkins or GitHub Actions.
• Strong command of SQL/MySQL and scripting with Bash/Shell.
• Experience working with external clients or in client-facing roles

.

Preferred Qualifications:

• AWS Certification (e.g., AWS Certified Developer or DevOps Engineer).
• Familiarity with Agile/Scrum methodologies.
• Strong analytical and problem-solving skills.
• Excellent communication and stakeholder management abilities.

The ideal candidate should be a proactive individual with strong communication skills and a team player. He or she should have an experience of at least 1-2 years in Java development.

1. Hands on experience in designing and developing applications using Java EE platforms and at least 1-2 years’ experience on the same.
2. Should have excellent in-depth knowledge on Core Java
3. Experience in multi-threaded server-side Java programming
4. Working knowledge on web services.
5. Working knowledge on hibernate and springs framework.
6. Added advantage if worked in any of the BPM tools
7. Should have worked in SDLC environment.
8. Good communication skills.
9. Experience in working effectively within a team.

Responsibilities:

• Designing and implementing Java-based applications.
• Analyzing user requirements to inform application design.
• Defining application objectives and functionality.
• Aligning application design with business goals.
• Developing and testing software.
• Debugging and resolving technical problems that arise.
• Producing detailed design documentation.
• Recommending changes to existing Java infrastructure.
• Developing multimedia applications.
• Developing documentation to assist users.
• Ensuring continuous professional self-development.
• 
  

Requirements

• Degree in Computer Science or related field.
• Experience with user interface design, database structures, and statistical analyses.
• Analytical mindset and good problem-solving skills.
• Excellent written and verbal communication.
• Good organizational skills.
• Skills required- Java , Springboot , Hibernate , Database

Looking for Java_ Spring boot & Microservices Developers.

Experience: 3-12 Years

Location: Hyderabad

Employment Type: Fulltime

Notice Period: Immediate to 30 Days

Roles & Responsibilities:

• Strong programming expertise in java and j2ee.
• Strong project experience in Spring & Spring boot.
• Good with multithreading, collections.
• Good work experience in Microservices.
• Strong with Data Structures and Algorithms.
• Good with Design and Design Patterns.

Required Skills:

• BS/MS degree in Computer Science, Engineering or a related subject.
• Proven hands-on 3+ years Software Product Development experience.
• Proven working experience in Java development.
• Hands on experience in designing and developing applications using Java EE platforms.
• Object Oriented analysis and design using common design patterns.
• Excellent knowledge of Relational Databases and SQL.
• Very good communication skills.
• Strong analytical and logical skills.

Requirements:

Spring Boot + Google Cloud Exp 4+ Years Strong Spring 4+ Years Restful 4+ Years J2EE 4+ Years Core Java 4+ Years

• Must have hands-on experience in Java 8 or higher / J2EE
• Understanding of Agile and Lean software development processes and practices.
• Experience in Spring Boot and other spring frameworks
• Experience in developing web applications using Spring MVC
• Experience in MongoDB/ Kafka / RabbitMQ
• Microservices
• Collaborate with other team members and stakeholders
• Mandatory Skills: Core Java, Spring/Spring Boot, REST, Hibernate

The candidate for the role will be responsible for enabling single view for the data from multiple sources. 

• Work on creating data pipelines to graph database from data lake
• Design graph database
• Write Graph Database queries for front end team to use for visualization
• Enable machine learning algorithms on graph databases
• Guide and enable junior team members

B.Tech with 2-7 years of experience

Must Have

Hands-on exposure to Graph Databases like Neo4J, Janus etc..

1. Hands-on exposure to programming and scripting language like Python and PySpark
2. Knowledge of working on cloud platforms like GCP, AWS etc.
3. Knowledge of Graph Query languages like CQL, Gremlin etc.
4. Knowledge and experience of Machine Learning 

Good to Have

• Knowledge of working on Hadoop environment

• Knowledge of graph algorithms

• Ability to work on tight deadlines