Overview
Skills
Job Details
Role: Full stack Tech Lead 
Location: Remote (US) should be okay prefer in PST zone
Duration:.( Fulltime preferred)
Tech Lead   FSD Product Development
We are seeking a Tech Lead to join our FSD Product Development team. This is a great opportunity for someone who specializes in building large-scale, cloud-based big data and MLOps platforms and APIs to access timely, accurate, and relevant data.
An ideal candidate will have built scalable platforms that integrate diverse data sources and provide comprehensive data capabilities to all of our products and delivery channels.
Responsibilities
What you ll be doing:
  Partner with Architecture/Product/CloudOps/Engineering teams to craft highly scalable, flexible, and resilient cloud architectures that address customer business problems and accelerate the adoption of cloud services.
  Design and implement complex architectural solutions using AWS design principles, best practices, and industry standards.
  Build scalable, reliable, and cost-efficient ML pipelines using Python, AWS services (Sage Maker, Lambda, Step Functions, S3, ECR, etc.), and container technologies (Docker, ECS/Faregate).
  Lead technical design reviews, guide engineering teams on architectural best practices, and create high-level and low-level design documents.
  Determine code quality and test coverage; design and implement tests to ensure high-quality software.
  Communicate and explain technical/architectural decisions to product, development, and delivery teams.
  Drive continual improvement in quality and efficiency, including defect prevention/root cause analysis, and suggest improvements to technology and efficiency.
  Perform proof of concept (PoC) work for integrating new technologies into the existing product.
  Comprehend detailed project specifications and adapt to various technologies while working on multiple projects simultaneously.
  Participate in code reviews of software engineers to deliver high-quality solutions.
  Collaborate closely with the product team and actively participate in business requirement analysis.
  Lead and mentor junior team members, providing technical direction and coaching.
  Research and implement performance tuning and enhancements for optimal infrastructure usage.
Knowledge, Experience & Qualifications
What your background looks like:
  BS in Computer Science or related fields; MS preferred.
  8+ years of experience in key engineering roles (Tech Lead, Software Engineer, Software Architect).   5+ years of experience with Amazon Web Services (AWS), architecting and deploying cloud-native solutions.
  Deep understanding of cloud computing, workload transition, AWS Well-Architected Framework, industry standards, and best practices.   Strong experience with MLOps platforms: AWS Sage Maker, Kubeflow, or MLflow.   Hands-on design and development using Python, Flask, Django, AsyncIO, etc.   Solid understanding of distributed systems, integration, testing, and troubleshooting.   Experience with monitoring distributed systems, and strategies for error detection and recovery.   Experience designing and developing APIs, Real-Time Systems, and Microservices.   Familiarity with AWS services: EKS, S3, RDS, Lambda, Aurora, ECS-Fargate.
  Eagerness to learn new frameworks and build new processes from scratch.
  Demonstrated familiarity with CI/CD processes and tools (CodeCommit, Code Deploy, Code Pipeline, Jenkins, Harness, etc.).
  Experience integrating with async messaging/logging/queues: Kafka, RabbitMQ, or SQS.
  Strong knowledge of software development processes and project management methodologies.
  Excellent problem-solving, analytical, communication, and documentation skills.
  Ability to lead cross-functional initiatives and work effectively in dynamic environments.
  Collaborative mindset, comfortable working with globally distributed teams.
Nice to Have
  Experience with monitoring/logging tools such as Dynatrace and Splunk.
  Familiarity with ML frameworks: TensorFlow, PyTorch, scikit-learn.
  Experience with orchestration tools: Kubeflow, MLflow, Airflow, etc.
  Background in building automated/scheduled pipelines for analytical processes.