TrumetricAI

Java Tech Lead (5–6 Years Experience)

About the Role

We are seeking a highly skilled Java Tech Lead with 5–6 years of hands-on experience in backend engineering, architecture design, and leading development teams. 

The ideal candidate will combine strong technical expertise in Java frameworks with a deep understanding of system design, scalability, and performance optimization.

This role involves technical leadership, code reviews, and architectural decision-making for complex enterprise systems — with occasional exposure to analytics-driven and Python-based components.

Key Responsibilities

• Architect, design, and develop scalable backend systems using Java (Quarkus, Spring Boot, Spring, Java EE).
• Own the architecture — ensure modular, extensible, and high-performance service design.
• Lead and mentor a team of developers; conduct code reviews, enforce best practices, and ensure high code quality.
• Collaborate with cross-functional teams (frontend, DevOps, product, data) to deliver integrated, end-to-end solutions.
• Design and optimize database schemas (MySQL, PostgreSQL) and ensure efficient query performance.
• Implement and maintain microservices and distributed systems with strong fault tolerance and observability.
• Drive the adoption of modern development workflows — Git branching strategy, CI/CD, and code quality automation.
• Analyze system performance bottlenecks, implement monitoring, and ensure smooth production deployments.
• Contribute to architecture reviews, technical documentation, and design discussions.
• Occasionally contribute to Python-based analytics modules or automation scripts.
• Work with AWS cloud services (EC2, S3, RDS, Lambda) for deployment, scaling, and infrastructure automation.

Required Skills & Qualifications

• 5–6 years of professional experience in backend application development using Java.
• Strong proficiency in Java frameworks: Quarkus, Spring Boot, Spring, Java EE.
• Proven experience in architecture design, system decomposition, and microservices design principles.
• Solid understanding of object-oriented design (OOD), design patterns, and SOLID principles.
• Strong experience with relational databases (MySQL, PostgreSQL) and query optimization.
• Good understanding of event-driven systems, RESTful APIs, and asynchronous processing.
• Proficiency in Git for version control and team collaboration.
• Strong analytical and debugging skills; ability to diagnose complex production issues.

Good to Have

• Hands-on experience with Python for data processing or analytics integrations.
• Familiarity with AWS cloud architecture and cost optimization practices.
• Experience with CI/CD pipelines (GitHub Actions, Jenkins, GitLab CI).
• Knowledge of Docker/Kubernetes for containerized deployments.
• Exposure to NoSQL databases (MongoDB, DynamoDB, Cassandra).
• Experience with message queues (Kafka, RabbitMQ, or AWS SQS).
• Understanding of system scalability, caching (Redis/Memcached), and observability stacks (Prometheus, Grafana, ELK).

Soft Skills

• Strong leadership, mentoring, and communication skills.
• Proven ability to drive technical decisions and balance short-term delivery with long-term architectural health.
• Collaborative mindset — works closely with product, design, and operations teams.
• Passion for clean architecture, high performance, and continuous improvement.
• Self-driven with a strong sense of ownership and accountability.

Senior Python Developer

Experience: 4–8 Years

About the Role

We are looking for a Senior Python Developer Engineer to join our team. This role focuses on building and maintaining data-intensive backend systems, handling large-scale datasets, and exposing insights through robust, scalable APIs.

You will work closely with operational and transactional data, design efficient data pipelines, and build backend services that power analytics, reports, and ERP workflows. The ideal candidate is strong in Python, excellent with data and databases, and capable of owning features end-to-end.

Key Responsibilities

• Analyze large datasets to identify trends, inconsistencies, and operational insights.
• Design, build, and maintain backend services and REST APIs using Python and FastAPI.
• Perform advanced data manipulation and aggregation using Pandas, NumPy, and SQL.
• Design and optimize data pipelines for analytics, reporting, and downstream systems.
• Implement automated data quality checks, validations, and monitoring scripts.
• Work closely with product, application, and business teams to translate raw data into clear, actionable outputs.
• Optimize query performance across relational and analytical databases.
• Expose processed data and insights via APIs or dashboards for consumption by web or ERP applications.
• Ensure high standards of code quality, performance, scalability, and maintainability.
• Write clear documentation for APIs, data flows, and processing logic.

Required Skills & Qualifications

• 4–8 years of strong, hands-on experience with Python in production systems.
• Excellent experience with data handling, processing, and large datasets.
• Strong experience building APIs using FastAPI (or similar frameworks).
• Deep expertise in Pandas, NumPy, and SQL.
• Solid experience with MySQL and PostgreSQL.
• Experience working with analytical or reporting workloads.
• Strong understanding of data modeling, joins, aggregations, and performance tuning.
• Proficiency with Git and collaborative development workflows.
• Strong analytical and problem-solving skills with the ability to work independently.

Good to Have

• Experience with ClickHouse, Databricks, or Elasticsearch.
• Exposure to data engineering concepts such as ETL/ELT, batch processing, and data pipelines.
• Experience with workflow orchestration tools (Airflow, Prefect, Dagster).
• Familiarity with data visualization libraries (Plotly, Matplotlib, Seaborn).
• Experience with AWS services (S3, EC2, RDS, Lambda).
• Prior experience integrating data services into ERP or business applications.

Soft Skills

• Strong analytical mindset and attention to detail.
• High ownership and accountability.
• Ability to work independently with minimal supervision.
• Clear communication and documentation skills.
• Proactive, solution-oriented approach.

Senior Machine Learning Engineer

About the Role

We are looking for a Senior Machine Learning Engineer who can take business problems, design appropriate machine learning solutions, and make them work reliably in production environments.

This role is ideal for someone who not only understands machine learning models, but also knows when and how ML should be applied, what trade-offs to make, and how to take ownership from problem understanding to production deployment.

Beyond technical skills, we need someone who can lead a team of ML Engineers, design end-to-end ML solutions, and clearly communicate decisions and outcomes to both engineering teams and business stakeholders. If you enjoy solving real problems, making pragmatic decisions, and owning outcomes from idea to deployment, this role is for you.

What You’ll Be Doing

Building and Deploying ML Models

• Design, build, evaluate, deploy, and monitor machine learning models for real production use cases.
• Take ownership of how a problem is approached, including deciding whether ML is the right solution and what type of ML approach fits the problem.
• Ensure scalability, reliability, and efficiency of ML pipelines across cloud and on-prem environments.
• Work with data engineers to design and validate data pipelines that feed ML systems.
• Optimize solutions for accuracy, performance, cost, and maintainability, not just model metrics.

Leading and Architecting ML Solutions

• Lead a team of ML Engineers, providing technical direction, mentorship, and review of ML approaches.
• Architect ML solutions that integrate seamlessly with business applications and existing systems.
• Ensure models and solutions are explainable, auditable, and aligned with business goals.
• Drive best practices in MLOps, including CI/CD, model monitoring, retraining strategies, and operational readiness.
• Set clear standards for how ML problems are framed, solved, and delivered within the team.

Collaborating and Communicating

• Work closely with business stakeholders to understand problem statements, constraints, and success criteria.
• Translate business problems into clear ML objectives, inputs, and expected outputs.
• Collaborate with software engineers, data engineers, platform engineers, and product managers to integrate ML solutions into production systems.
• Present ML decisions, trade-offs, and outcomes to non-technical stakeholders in a simple and understandable way.

What We’re Looking For

Machine Learning Expertise

• Strong understanding of supervised and unsupervised learning, deep learning, NLP techniques, and large language models (LLMs).
• Experience choosing appropriate modeling approaches based on the problem, available data, and business constraints.
• Experience training, fine-tuning, and deploying ML and LLM models for real-world use cases.
• Proficiency in common ML frameworks such as TensorFlow, PyTorch, Scikit-learn, etc.

Production and Cloud Deployment

• Hands-on experience deploying and running ML systems in production environments on AWS, GCP, or Azure.
• Good understanding of MLOps practices, including CI/CD for ML models, monitoring, and retraining workflows.
• Experience with Docker, Kubernetes, or serverless architectures is a plus.
• Ability to think beyond deployment and consider operational reliability and long-term maintenance.

Data Handling

• Strong programming skills in Python.
• Proficiency in SQL and working with large-scale datasets.
• Ability to reason about data quality, data limitations, and how they impact ML outcomes.
• Familiarity with distributed computing frameworks like Spark or Dask is a plus.

Leadership and Communication

• Ability to lead and mentor ML Engineers and work effectively across teams.
• Strong communication skills to explain ML concepts, decisions, and limitations to business teams.
• Comfortable taking ownership and making decisions in ambiguous problem spaces.
• Passion for staying updated with advancements in ML and AI, with a practical mindset toward adoption.

Experience Needed

• 6+ years of experience in machine learning engineering or related roles.
• Proven experience designing, selecting, and deploying ML solutions used in production.
• Experience managing ML systems after deployment, including monitoring and iteration.
• Proven track record of working in cross-functional teams and leading ML initiatives.

Key Responsibilities:

• Design, implement, and maintain scalable, secure, and cost-effective infrastructure on AWS and Azure
• Set up and manage CI/CD pipelines for smooth code integration and delivery using tools like GitHub Actions, Bitbucket Runners, AWS Code build/deploy, Azure DevOps, etc.
• Containerize applications using Docker and manage orchestration with Kubernetes, ECS, Fargate, AWS EKS, Azure AKS.
• Manage and monitor production deployments to ensure high availability and performance
• Implement and manage CDN solutions using AWS CloudFront and Azure Front Door for optimal content delivery and latency reduction
• Define and apply caching strategies at application, CDN, and reverse proxy layers for performance and scalability
• Set up and manage reverse proxies and Cloudflare WAF to ensure application security and performance
• Implement infrastructure as code (IaC) using Terraform, CloudFormation, or ARM templates
• Administer and optimize databases (RDS, PostgreSQL, MySQL, etc.) including backups, scaling, and monitoring
• Configure and maintain VPCs, subnets, routing, VPNs, and security groups for secure and isolated network setups
• Implement monitoring, logging, and alerting using tools like CloudWatch, Grafana, ELK, or Azure Monitor
• Collaborate with development and QA teams to align infrastructure with application needs
• Troubleshoot infrastructure and deployment issues efficiently and proactively
• Ensure cloud cost optimization and usage tracking

Required Skills & Experience:

• 3-4 years of hands-on experience in a DevOps
• Strong expertise with both AWS and Azure cloud platforms
• Proficient in Git, branching strategies, and pull request workflows
• Deep understanding of CI/CD concepts and experience with pipeline tools
• Proficiency in Docker, container orchestration (Kubernetes, ECS/EKS/AKS)
• Good knowledge of relational databases and experience in managing DB backups, performance, and migrations
• Experience with networking concepts including VPC, subnets, firewalls, VPNs, etc.
• Experience with Infrastructure as Code tools (Terraform preferred)
• Strong working knowledge of CDN technologies: AWS CloudFront and Azure Front Door
• Understanding of caching strategies: edge caching, browser caching, API caching, and reverse proxy-level caching
• Experience with Cloudflare WAF, reverse proxy setups, SSL termination, and rate-limiting
• Familiarity with Linux system administration, scripting (Bash, Python), and automation tools
• Working knowledge of monitoring and logging tools
• Strong troubleshooting and problem-solving skills

Good to Have (Bonus Points):

• Experience with serverless architecture (e.g., AWS Lambda, Azure Functions)
• Exposure to cost monitoring tools like CloudHealth, Azure Cost Management
• Experience with compliance/security best practices (SOC2, ISO, etc.)
• Familiarity with Service Mesh (Istio, Linkerd) and API gateways
• Knowledge of Secrets Management tools (e.g., HashiCorp Vault, AWS Secrets Manager)