Custom Software Engineer
Project Role Description : Develop custom software solutions to design, code, and enhance components across systems or applications. Use modern frameworks and agile practices to deliver scalable, high-performing solutions tailored to specific business needs.
Must have skills : Machine Learning Operations
Good to have skills : NA
Minimum 5 year(s) of experience is required
Educational Qualification : 15 years full time education
Summary:
As a Data Ops, ML Engineer, you will play a critical role in designing, building, and maintaining production-grade data and machine learning systems that power intelligent enterprise products and services. The individual will lead the development of scalable data pipelines, model deployment frameworks, and MLOps infrastructure to enable reliable, secure, and high-performing AI capabilities across business value streams.
ESSENTIAL JOB FUNCTIONS AND RESPONSIBILITIES:
Data Pipeline Development: Build and maintain robust pipelines to collect, process, and transform structured and unstructured data for model training and inference.
ML Model Deployment: Design, deploy, and manage machine learning models in production environments with a focus on automation, scalability, and performance.
CI/CD for ML Workflows: Develop and maintain continuous integration and delivery of pipelines for data and ML workflows, including testing, monitoring, and automated retraining.
MLOps Infrastructure: Implement and refine infrastructure components such as feature stores, model registries, and orchestration frameworks.
Cross-Functional Collaboration: Partner with data scientists, product managers, and engineering leads to translate experimental models into production-ready services.
Observability & Monitoring: Create frameworks to monitor data pipelines and ML models for drift, anomalies, and performance degradation.
Optimization: Tune model serving for latency and cost efficiency across batch, streaming, and real-time environments.
Automation: Identify and automate repetitive tasks to improve reliability and development velocity.
Governance & Compliance: Manage data lineage, versioning, and governance to ensure reproducibility and regulatory compliance.
Troubleshooting: Diagnose and resolve issues in real-time production environments, ensuring minimal disruption to business operations.
KNOWLEDGE, SKILLS, AND ABILITIES:
5+ years of hands-on experience in data engineering, ML engineering, or backend software engineering for AI-driven products.
Strong programming skills in Python, SQL, and at least one compiled language (e.g., Go, Java, Scala).
Proven experience with MLOps tools and platforms (e.g., MLflow, Airflow, Kubeflow, SageMaker, AI, Databricks).
Deep understanding of data processing frameworks (Spark, Flink, Beam) and cloud architectures (AWS, Azure, GCP).
Experience with containerization and orchestration (Docker, Kubernetes).
Familiarity with real-time data processing and event-driven systems (Kafka, Pub/Sub).
Expertise in feature store management, model versioning, and pipeline observability.
Knowledge of DevOps and infrastructure-as-code tools (Terraform, CloudFormation).
Strong analytical and problem-solving skills with attention to detail.
Effective communication and collaboration skills across technical and business teams.
EDUCATION AND TRAINING:
Bachelor s degree in Computer Science, Engineering, or a related technical field.
5+ years of experience in data engineering, ML engineering, or backend software development for AI-driven products.
Experience with CRM and ERP systems (e.g., Salesforce, Workday) preferred.
Experience implementing Responsible AI principles, including bias monitoring and explainability.
Other Qualifications
The Winning Way behaviors that all employees need in order to meet the expectations of each other, our customers, and our partners.
Communicate with Clarity - Be clear, concise and actionable. Be relentlessly constructive. Seek and provide meaningful feedback.
Act with Urgency - Adopt an agile mentality - frequent iterations, improved speed, resilience. 80/20 rule – better is the enemy of done. Don t spend hours when minutes are enough.
Work with Purpose - Exhibit a We Can mindset. Results outweigh effort. Everyone understands how their role contributes. Set aside personal objectives for team results.
Drive to Decision - Cut the swirl with defined deadlines and decision points. Be clear on individual accountability and decision authority. Guided by a commitment to and accountability for customer outcomes.
Own the Outcome - Defined milestones, commitments and intended results. Assess your work in context, if you re unsure, ask. Demonstrate unwavering support for decisions.
COMMENTS:
The above statements are intended to describe the general nature and level of work being performed by individuals in this position. Other functions may be assigned, and management retains the right to add or change the duties at any time.
Hyderabad
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