Senior Consultant - Python, Java with GCP

enGen Global; Hyderabad, Chennai; On-site; INR 3,500,000 - 5,500,000; Full time

Job summary

enGen Global seeks a hands-on Senior Consultant to design, build, test, deploy, and support secure, scalable enterprise applications using Python and Java on Google Cloud Platform. The role emphasizes AI/ML integration, cloud-native APIs, and robust security controls to meet production requirements.

You will collaborate with architects, product teams, data specialists, security, testing, and operations, mentoring peers and delivering production-ready solutions across Agile iterations.

Qualifications

• 8–10 years of hands-on software development experience in enterprise applications.
• Strong Python and Java production-grade delivery with cloud-native design.
• Experience integrating AI/ML into production using validated models and enterprise controls.

Responsibilities

• Design, build, test, deploy, and support enterprise apps on GCP.
• Develop REST APIs and event-driven services with secure integration.
• Implement CI/CD pipelines and infrastructure-as-code for automated deployment.
• Collaborate with architects, product teams, data scientists, security, and operations.

Skills

Python

Java

GCP

AI/ML

APIs

CI/CD

Docker

Kubernetes

Security

Observability

Education

Bachelor's or Master's in CS/IT/Engineering

Tools

Docker

Kubernetes

GKE

Vertex AI

CI/CD Tools

Job description

Job Title: Senior Consultant - Advanced Technologies

Primary Technology: GCP Cloud, Artificial Intelligence, Python and Java Application Engineering

Role Overview

We are seeking a hands-on Senior Developer with 8-10 years of software engineering experience to design, build, test, deploy, and support secure, scalable, and maintainable enterprise applications. The role requires strong development expertise in Python and Java, practical experience with Google Cloud Platform (GCP), and the ability to integrate AI and machine-learning capabilities into production solutions. The developer will translate technical designs into reliable software, build cloud-native services and APIs, contribute to data and AI workflows, apply engineering standards, and collaborate with architects, product teams, data specialists, security, testing, and operations teams.

Key Responsibilities

• Design, develop, test, deploy, and support enterprise applications and cloud-native services using Python and Java.
• Build secure, scalable solutions on GCP using appropriate compute, storage, networking, identity, messaging, data, and observability services.
• Develop REST APIs, event-driven services, asynchronous processing components, and reusable integration interfaces.
• Integrate AI, machine-learning, and generative AI capabilities into applications using approved models, services, APIs, and enterprise controls.
• Implement data ingestion, transformation, validation, and consumption logic required by application, analytics, and AI use cases.
• Apply object-oriented design, design patterns, clean-code practices, modular architecture, and effective error-handling strategies.
• Create automated unit, integration, API, and component tests; participate in peer reviews and resolve quality or security findings.
• Build and maintain CI/CD pipelines, infrastructure-as-code components, deployment configurations, and automated quality controls.
• Containerize workloads using Docker and deploy or support applications on Kubernetes and Google Kubernetes Engine (GKE).
• Implement logging, metrics, tracing, alerting, resilience, retry, timeout, and recovery patterns for production services.
• Troubleshoot complex application, cloud, integration, data, and performance issues and perform root-cause analysis.
• Contribute to technical design documents, API specifications, data mappings, runbooks, release instructions, and support documentation.
• Collaborate with architects, product owners, business analysts, data engineers, AI/ML engineers, testers, security, and operations teams.
• Mentor developers, support coding standards, estimate development work, and help deliver production-ready solutions across Agile iterations.

Mandatory Technical Skills

• Python Development Python, FastAPI or Flask, package management, asynchronous programming, testing frameworks Advanced hands-on development skills for production services, APIs, automation, data processing, exception handling, performance, and maintainable Python code.
• Java Development Java, Spring Boot, REST services, dependency injection, concurrency, testing frameworks Strong experience building secure, reliable, and maintainable enterprise services using modern Java engineering practices.
• Google Cloud Platform GCP compute, Cloud Run, GKE, Cloud Functions, storage, networking, IAM, Pub/Sub, monitoring Hands-on experience developing and operating scalable applications on GCP while applying security, availability, observability, and cost-conscious patterns.
• AI Machine Learning Integration Generative AI APIs, Vertex AI concepts, model integration, prompt orchestration, embeddings, responsible AI controls Ability to integrate AI/ML capabilities into applications, implement guardrails and validation, and support monitored, reliable production behavior.
• API Integration REST APIs, API specifications, gateways, authentication, authorization, messaging, event-driven integration Builds reusable and secure interfaces with appropriate contracts, versioning, validation, idempotency, throttling, error handling, and observability.
• Data Database Development SQL, relational databases, NoSQL databases, transactions, indexing, data modeling, query optimization Develops reliable data-access layers and selects suitable persistence patterns based on consistency, scale, latency, and query requirements.
• Cloud-Native Engineering Microservices, distributed systems, resilience patterns, asynchronous processing, configuration management Understands service boundaries, scalability, failure handling, retries, timeouts, circuit breakers, and production operability.
• Containers Orchestration Docker, Kubernetes, Google Kubernetes Engine (GKE), Helm concepts, secrets and configuration Builds containerized workloads and supports secure deployment, scaling, service networking, health checks, and operational readiness.
• DevSecOps Automation Git, CI/CD, infrastructure as code, automated testing, code quality, vulnerability scanning Contributes to repeatable builds and deployments with automated quality, security, traceability, and release controls.
• Observability Production Support Logging, metrics, tracing, alerting, debugging, profiling, root-cause analysis Diagnoses application and cloud issues, improves service health, and supports stable production operations.
• Security Engineering IAM, secure coding, secrets management, encryption, input validation, dependency security Applies least privilege, protects sensitive data, addresses common application risks, and follows enterprise security requirements.
• Engineering Collaboration Agile delivery, technical documentation, code reviews, estimation, mentoring, stakeholder communication Works effectively across product and technology teams, communicates technical decisions, and guides developers toward production-quality outcomes.

Preferred Qualifications

• Hands-on experience delivering enterprise-scale applications or modernization initiatives on GCP.
• Experience integrating generative AI or machine-learning services into production applications with appropriate validation, monitoring, and security controls.
• Experience with event-driven architecture, messaging, streaming, or cloud integration patterns.
• Working knowledge of relational and NoSQL databases, data pipelines, and analytics-oriented application integration.
• Experience with Kubernetes/GKE, infrastructure as code, automated delivery pipelines, and production observability.
• Google Cloud, Java, Python, Kubernetes, or AI/ML certifications are advantageous but not mandatory.

Education

• Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, or a related discipline, or equivalent relevant professional experience.

Key Competencies

• Hands-on software engineering; analytical problem solving; cloud-native development; secure coding; quality ownership; production support discipline; learning agility; clear technical communication; mentoring; collaboration; ownership and accountability.