Improving

Data Engineer - Remote

Nearshore engineer on a team converting SAS code to Python and SQL on Databricks using generative AI. You will work with the existing accelerators, own deliverables end to end, and communicate directly with client and partner stakeholders.

Required for both:

4+ years of professional data or software engineering experience

Strong Python and SQL

Hands-on Databricks (Unity Catalog, Workflows, Databricks Asset Bundles)

GitLab CI/CD: pipelines, merge request workflows, automated testing

Git branching and code review discipline

Clear written and spoken English with client-facing partners

Demonstrated ownership: scoping, delivering, and flagging risk without prompting

Focus: Pipeline reliability, validation, and delivery of converted code.

Responsibilities:

Build and run the pipelines that process SAS inventories and converted outputs

Validate converted code for parity against SAS outputs (row counts, checksums, schema, data types)

Own deployment through DABs and GitLab CI/CD

Manage Unity Catalog objects, permissions, and environment promotion

Troubleshoot job failures and performance issues

Required:

Spark and Delta Lake performance tuning

Data validation and reconciliation experience

Infrastructure as code or DAB-based deployment experience

Nice to have:

SAS reading ability, healthcare data exposure, Azure.

Technical Skills -

• 4–6 years of experience in Golang development, backend engineering, or automation testing. 
• Strong hands-on proficiency in Golang and backend development concepts. 
• Hands-on experience with Kubernetes and containerized environments. 
• Experience developing automation frameworks, unit tests, integration tests, and API validation tools using Go. 
• Strong understanding of distributed systems, system design, and cloud-native architecture. 
• Experience in validating backend services, REST/gRPC APIs, and Kubernetes-based platforms. 
• Familiarity with Docker, CI/CD pipelines, Jenkins, and GitHub Actions. 
• Knowledge of cloud-native observability tools such as Prometheus and Grafana. 
• Strong debugging, analytical, and problem-solving skills. 
• Ability to contribute to backend development, reliability improvements, and performance validation. 

Key Responsibilities -

• Develop automation frameworks, unit tests, integration tests, and validation tools using Golang. 
• Validate backend services, distributed systems, REST/gRPC APIs, and Kubernetes-based platforms. 
• Contribute to backend development, debugging, reliability improvements, and performance validation. 
• Work with cloud-native technologies such as Kubernetes, Docker, Prometheus, and Grafana. 
• Automate workflows and testing through CI/CD pipelines using Jenkins/GitHub Actions. 
• Collaborate with engineering teams to improve system quality, observability, and test coverage. 

Good to Have -

• Experience with infrastructure platforms or storage systems. 
• Exposure to AWS, Azure, or GCP. 
• Experience with performance, load, or resiliency testing. 
• Familiarity with Ginkgo, Gomega, or similar Go testing frameworks. 

Position Summary

We are seeking an experienced Senior Golang Developer with deep expertise in Kubernetes controllers and operators to join our Cloud & Infrastructure Engineering team. You will design, develop, and maintain robust Kubernetes-native applications and custom operators that power our clients' containerized infrastructure. You should have 4+ years of professional Golang experience with demonstrated proficiency building production-grade Kubernetes controllers and operators.

Key Responsibilities

Design and develop custom Kubernetes controllers and operators to extend platform functionality

Write clean, efficient, maintainable Golang code following industry best practices and design patterns

Implement and manage Custom Resource Definitions (CRDs) for Kubernetes platforms

Develop API clients and libraries for Kubernetes resource management

Collaborate with DevOps and Platform teams to integrate operators into production environments

Implement comprehensive unit and integration testing strategies for controller logic

Troubleshoot and debug complex issues in Kubernetes environments

Contribute to architectural decisions and technical documentation

Mentor junior developers and participate in code reviews

Required Qualifications

4+ years of professional experience developing applications in Golang

2+ years of hands-on experience building and maintaining Kubernetes controllers and/or operators (e.g., using Kubebuilder, Operator SDK, or controller-runtime)

Strong understanding of Kubernetes architecture, APIs, and resource management

Proficiency with Custom Resource Definitions (CRDs) and API conventions

Experience with containerization technologies (Docker, container registries)

Solid understanding of concurrent programming and goroutines

Experience with version control systems (Git) and CI/CD pipelines

Demonstrated ability to write unit tests and integration tests

Title: AI/ML Test Engineer – GenAI

Location - Mumbai

Technical Skills

• Strong experience in Generative AI, LLMs, and Agentic AI systems

• Hands-on expertise with AI evaluation frameworks (RAGAS, DeepEval, TruLens, LangSmith, Promptfoo, etc.)

• Proficiency in Python and AI/ML development libraries

• Knowledge of Prompt Engineering, prompt testing, and optimization

Ability to define and track evaluation metrics such as accuracy, relevance, groundedness, hallucination rate, latency, and user satisfaction

• Experience in creating automated evaluation pipelines and benchmarking frameworks

• Strong understanding of AI safety, guardrails, bias testing, and responsible AI practices

• Familiarity with REST APIs, JSON, vector databases, and knowledge retrieval systems

• Experience in A/B testing, human-in-the-loop evaluation, and red teaming

• Strong experience in Manual Testing of AI/GenAI applications, including functional, exploratory, UAT, regression, and end-to-end testing

• Expertise in validating Agent Reasoning, Tool Calling, Workflow Execution, and Response Quality

• Hands-on experience in Automation Testing using Python frameworks

Key Responsibilities

• Design, execute, and automate evaluation strategies for Agentic AI applications.
• Develop evaluation datasets, test cases, and benchmark suites.
• Measure and improve agent performance, reasoning quality, tool usage, and workflow effectiveness.
• Analyze model outputs and identify hallucinations, biases, safety risks, and failure patterns.
• Collaborate with AI Engineers, Product Teams, and Domain Experts to improve agent quality and reliability.
• Generate evaluation reports, dashboards, and actionable recommendations.

Essential Duties and Responsibilities:

• Build new automations in Python: API integrations, data pipelines, scheduled jobs, and process replacements scoped with operating partners.

• Maintain the existing Power Automate estate, both unattended cloud and desktop flows. Triage failures, repair flows, and keep unattended runs healthy on the bot-server farm. Operate the Power Platform space around them: environments, solutions, connection references, and pipeline-managed deployments.

• Migrate Power Automate flows to Python where the economics favor it. Retire flows rather than patching them indefinitely.

• Integrate systems over REST APIs. Handle JSON and XML transformation, authentication (OAuth, service principals), and error handling that survives flaky endpoints.

• Author SQL queries, tables, and stored procedures that support automations.

• Operate what you build. Instrument jobs with logging, monitoring, and alerting so failures surface before the business notices them. Write runbooks.

• Improve how automations run. Today they run as scheduled jobs on VMs. Help evaluate and move toward containerized or Azure-native execution (Functions, Container Apps) where it reduces operational load.

• Use AI coding tools as a core part of daily development, within company governance and review standards.

• Document what you build so the next engineer, or an operating partner, can understand and extend it.

Knowledge, Skills and Abilities:

• Python proficiency: clean scripting, packaging, error handling, structured logging, and enough testing to trust a job running unattended at 2 a.m.

• Power Automate strength across cloud and desktop flows: able to read, debug, and repair complex unattended flows built by someone else, plus the platform administration around them. You do not need to love the platform. You do need to support it capably, including solo coverage when other developers are out.

• REST API integration experience, including authentication patterns and rate-limit handling.

• SQL Server competence: comfortable writing T-SQL and authoring queries, tables, and stored procedures through a reviewed, versioned release process.

• Working knowledge of Azure: DevOps pipelines at minimum; Functions, Container Apps, or AKS exposure a plus.

• Daily fluency with AI-assisted development. You should be able to describe, in concrete detail, how you structure work with an agentic coding tool: what you delegate, what you review, where it fails, and how you catch it.

• PowerShell and shell scripting for glue work on Windows and Linux hosts.

• Production instincts: idempotent jobs, retries with backoff, alerting thresholds that page on real problems and stay quiet otherwise.

• Plain written and verbal communication. You will work directly with non-technical process owners who need to understand what an automation does and what to do when it stops.

Training and Experience:

• 3 to 5 years in automation engineering, RPA, or software engineering roles with automations shipped to production and operated afterward.

• A track record you can walk through: what you built, what broke, and what you changed.

• Demonstrated, current use of AI coding tools in real work. Candidates will be asked to describe their workflow in specifics; vague answers end the conversation.