*** Will be required to report at Chennai Location Office *** About Invent Health At Invent Health, we intertwine risk adjustment analytics and machine learning with healthcare data to create actionable insights that bolster our clients’ business outcomes and create a tangible difference in their members’ lives. Our unique solutions provide accurate gap and chase lists, financial and utilization forecasting, gap reporting, and the ability to improve risk scores. In this way, we prevent revenue leakage and false positives all while prioritizing high risk groups. Job Description: To work in capacity of AWS Databricks, Python, Pyspark Hands ON developer Work with stakeholders for regular updates, requirement understanding and design discussions. Hands-on experience on designing and developing scripts for custom ETL processes and automation in AWS, data bricks, Python, Pyspark etc. Should be skilled in creating architecture and design documentation and knowledge of data engineering tools & technologies Expertise in Cloud related Big Data integration and infrastructure Tec stack using Databricks & Apache Spark framework Well-versed with scripting languages – Python Well versed with Data Orchestration/ETL . Good SQL/PL SQL programming Skills – writing complex procedures and queries for Dataframes and Notebooks Very good data engineering mindset to build, maintain pipelines, and ability to closely monitor critical pipeline batch jobs and resolve failures on urgent basis. Manage and Mentor junior developers in adopting and implementing the data bricks workflows. Excellent communication skills is a must Minimum Qualifications: B.E./ master’s in computer science, Information systems, or Computer engineering, Systems Engineering with 6+ years of relevant experience Minimum of 3+ work experience using Databricks, spark and Databricks workflows, Autoloader. Knowledge of other scripting languages such as Scala, JavaScript, API development. Knowledge of other Data Orchestration/ETL tools - Alteryx, Snowflake, Azure Data Factory etc. Knowledge of any of BI Tools - Tableau, Power BI, Quick Sight.
Key Responsibilities Collaborate with development teams, product managers, data scientists, and analysts to ideate and build software solutions. Design scalable client-side and server-side architectures. Develop front-end applications using appealing, mobile-responsive designs. Build and manage robust, well-functioning databases and backend applications. Write secure, efficient, and effective APIs for seamless data and model integration. Troubleshoot, debug, and upgrade applications to maintain responsiveness and efficiency. Implement data security best practices for APIs, services, and data storage. Write comprehensive technical documentation for new and existing applications. Build full-stack Python applications and service wrappers for delivering predictive services on cloud infrastructure. Participate in designing scalable architectures and implementing AI/ML model serving for SAAS applications. Technical Requirements: Backend Development Proficiency in Python for backend services. Proven experience with Flask and Jinja templates for backend and front-end integration. Experience building and managing APIs to run AI/ML models efficiently. Hands-on experience developing applications with MongoDB and RDS . Frontend Development Strong knowledge of front-end languages and libraries, including HTML , CSS , JavaScript , XML , jQuery . Experience with modern JavaScript frameworks such as React , Node.js, and Angular . Experience with AGGrid for dynamic and scalable grid representations. Cloud & DevOps Experience deploying and managing applications on AWS EC2 . Strong understanding of application serving with Gunicorn behind Nginx reverse proxy. Familiarity with AWS Parameter Store for managing application secrets. Hands-on experience with AWS Lambda , S3 , CloudWatch , and RDS . Proficient with Docker for containerization and deployment of applications. Knowledge of CI/CD pipelines and deployment best practices. Expertise in monitoring, scaling, and optimizing application performance on cloud infrastructure. Security & Best Practices Understanding of data protection, compliance, and security best practices in application development and cloud environments. Qualifications Bachelor’s Degree in Computer Science, Engineering, Statistics, or a relevant field. 6+ years of proven experience as a Full Stack Developer or similar role in software development, preferably building both desktop and mobile applications. High-level analytical and problem-solving skills to address complex challenges in healthcare data. Nice to Have Familiarity with healthcare data standards and healthcare analytics. Experience in building HIPAA-compliant applications.
Position Overview The Director of Infrastructure, Cloud, and ML Operations is a critical role within Invent Health, responsible for architecting, scaling, securing, and managing the company’s technology platforms. This position blends technical expertise, strategic vision, and people leadership to ensure that all infrastructure and machine learning operations are robust, scalable, and aligned with the company’s growth ambitions. Key ResponsibilitiesInfrastructure Strategy & Architecture · Develop and execute a forward-looking infrastructure strategy across cloud and network environments, ensuring seamless alignment with business objectives and scalability requirements. · Design and maintain modern, scalable, and secure architectures supporting multi-tenancy, high availability, and disaster recovery. · Architect, implement, and operate cloud platforms (IaaS, PaaS, SaaS) for high performance, scalability, and security. Operational Excellence & Reliability · Ensure the operational reliability, uptime, and service availability of the SaaS platform, including rapid incident response and robust observability practices. · Implement monitoring and incident management best practices, utilizing KPIs to drive continuous improvement. DevOps & Automation incl. Cloud Ops · Advance DevOps practices by driving automation throughout CI/CD pipelines and adopting Infrastructure as Code (IaC) methodologies to accelerate deployments and enhance engineering efficiency. · Implement standardized processes for service provisioning, deployment, and maintenance, blending DevOps and SRE principles. · Automate cloud provisioning and management using IaC and DevOps methodologies to streamline deployments and reduce manual intervention. Customer Support & Incident Management · Diagnose and resolve customer issues and lead incident management processes. · Conduct root-cause analyses and oversee the remediation of critical incidents impacting service availability. Security & Compliance · Enforce security best practices and ensure compliance with relevant industry standards like SOC-2, HIPAA, HITRUST etc. · Implement and oversee security controls and compliance policies within cloud environments. · Lead disaster recovery planning, business continuity, and develop governance frameworks. Cross-functional Collaboration · Work closely with product, engineering, and executive teams to inform architectural decisions and support key company initiatives. · Manage vendor relationships, infrastructure budgets, and effectively communicate platform health and project outcomes to leadership. Team Leadership & Development · Build, mentor, and manage a high-performing engineering team, fostering a culture of innovation, accountability, and operational excellence. Specialized Operations AreaMachine Learning Operations (ML Ops) · Design and maintain ML infrastructure supporting the entire machine learning lifecycle: data ingestion, model training, validation, deployment, and monitoring. · Automate ML workflows with CI/CD pipelines tailored to machine learning models for reproducibility and reliability. · Monitor deployed models for performance, drift, and data quality, implementing alerting and retraining as required. · Ensure security, compliance, and governance of ML data, models, and endpoints in production. · Collaborate with data science and engineering teams to ensure smooth handoffs and operational support. · Optimize resource allocation for ML workloads, balancing cost, performance, and scalability in cloud-based environments. Qualifications · 10+ years’ experience in infrastructure, cloud, and (preferred 4 + years’ experience in machine learning operations). · Proficiency in cloud platforms (AWS preferred) DevOps tools, and Infrastructure as Code. · Strong knowledge of modern security and compliance standards for SaaS and ML operations. · Demonstrated success in team leadership and cross-functional collaboration. · Clear communication skills and the ability to translate technical concepts into business outcomes.
Associate Data Scientist - I Invent Health values creativity and the application of cutting-edge technology in AI and ML to enhance data quality, engineering efficiency, and overall productivity in the Healthcare Analytics domain. As a part of the Data Science team, you will play a crucial role in our data-driven initiatives. You will work closely with our data science team to extract meaningful insights from large and complex healthcare datasets. Responsibilities: Participating in end-to-end modelling projects, handling large variety and volume of data. Executing research and analytics, including data source identification, processing, model/algorithm development. Conducting exploratory data analysis, feature construction, and feature engineering. Performing Feature Engineering to create relevant features from raw data to enhance model performance and interpretability. Developing and evaluating advanced Predictive models using Python and Spark ML-Lib. Focusing on specialty areas such as Anomaly Detection, Time Series Forecasting, NLP and Generative AI. Deploying models and monitoring their performance post-deployment. Conducting proof-of-concept research to explore new modelling techniques and methodologies. Evaluating the performance of data science models using appropriate metrics and techniques. Collaborate with cross-functional teams (e.g., clinicians, biostatisticians, engineers) to ensure the successful implementation of data science solutions. Continuously seek to innovate and bring cutting-edge solutions to our customers. Developing high-quality data-driven solutions and collaborating effectively within a multidisciplinary team environment. Delivering presentations and reports on findings and translating complex concepts into actionable insights. Requirements: 2 - 4 years of Data Science experience. Previous experience in Healthcare domain preferred. Bachelors in a quantitative field (Operation Research, Computer Science, Engineering, Applied Math, Statistics, Physics, Analytics, etc.), Advanced courses in Business Analytics/Data Science/Data Analytics Strong problem-solving and analytical skills. Proficiency in Deep Learning libraries/frameworks like Kera’s and TensorFlow is a plus. Strong understanding of machine learning algorithms and statistical modelling techniques. Experience in handling large datasets and proficiency in data manipulation. Proficiency in PySpark is a plus. Proficiency in any BI tool like Tableau is a plus Ability to translate complex data science concepts into practical solutions. Excellent communication skills, both oral and written. Designing test strategies, plans and processes, methodologies and supporting documentation for functional and non-functional areas
The role of Data Scientist - Clinical Data Extraction & AI Integration in our healthcare technology team requires an experienced individual with 3-6 years of experience. As a Data Scientist in this role, you will be primarily focused on medical document processing and data extraction systems. You will have the opportunity to work with advanced AI technologies to create solutions that enhance the extraction of crucial information from clinical documents, thereby improving healthcare data workflows and patient care outcomes. Your key responsibilities will include designing and implementing statistical models for medical data quality assessment, developing predictive algorithms for encounter classification, and validation. You will also be responsible for building machine learning pipelines for document pattern recognition, creating data-driven insights from clinical document structures, and implementing feature engineering for medical terminology extraction. Furthermore, you will apply natural language processing (NLP) techniques to clinical text, develop statistical validation frameworks for extracted medical data, and build anomaly detection systems for medical document processing. Additionally, you will create predictive models for discharge date estimation, encounter duration, and implement clustering algorithms for provider and encounter classification. In terms of AI & LLM Integration, you will be expected to integrate and optimize Large Language Models via AWS Bedrock and API services, design and refine AI prompts for clinical content extraction with high accuracy, and implement fallback logic and error handling for AI-powered extraction systems. You will also develop pattern matching algorithms for medical terminology and create validation layers for AI-extracted medical information. Having expertise in the healthcare domain is crucial for this role. You will work closely with medical document structures, implement healthcare-specific validation rules, handle medical terminology extraction, and conduct clinical context analysis. Ensuring HIPAA compliance and adhering to data security best practices will also be part of your responsibilities. Proficiency in programming languages such as Python 3.8+, R, SQL, and JSON, along with familiarity with data science tools like pandas, numpy, scipy, scikit-learn, spaCy, and NLTK is required. Experience with ML Frameworks including TensorFlow, PyTorch, transformers, huggingface, and visualization tools like matplotlib, seaborn, plotly, Tableau, and PowerBI is desirable. Knowledge of AI Platforms such as AWS Bedrock, Anthropic Claude, OpenAI APIs, and experience with cloud services like AWS (SageMaker, S3, Lambda, Bedrock) will be advantageous. Familiarity with research tools like Jupyter notebooks, Git, Docker, and MLflow is also beneficial for this role.,