Superteams.ai
Website:
superteams.ai
Job details:
About UsSuperteams is building AI-powered products that help businesses solve real-world problems through intelligent automation and data-driven insights.
As we scale our AI product and work with a growing set of businesses, we are looking for a Product Implementation Lead – AI/ML to take ownership of deploying, integrating, and continuously improving our AI platform across customer environments.
This is a highly hands-on role combining AI/ML engineering, software engineering, cloud infrastructure, system integration, and product implementation. You will work closely with our Product and Engineering teams as well as customer technical teams to take solutions from architecture and experimentation through production deployment.
What You'll Do- Lead the end-to-end technical implementation of our AI/ML product across customer environments.
- Translate business and technical requirements into system architecture, implementation plans, and production-ready solutions.
- Design, build, evaluate, and optimize AI/ML and Generative AI workflows, including LLM-based applications, RAG pipelines, agents, embeddings, and model inference.
- Lead integrations with CRMs, APIs, databases, enterprise applications, authentication systems, and third-party services.
- Design and manage cloud deployments across AWS, GCP, or Azure, including compute, networking, storage, containers, and GPU infrastructure.
- Troubleshoot complex issues across the application, AI/ML, infrastructure, and integration layers.
- Build POCs and technical prototypes to evaluate new AI capabilities and move successful solutions towards production.
- Work closely with Product and Engineering to identify implementation gaps and convert recurring requirements into scalable product capabilities.
- Establish implementation, deployment, monitoring, and technical documentation practices as the product scales.
What We're Looking ForExperience & Skills- 6+ years of experience in software engineering, AI/ML engineering, cloud engineering, solutions architecture, or related technical roles.
- Strong software engineering background with hands-on experience building and deploying production-grade applications.
- At least 2–3 years of hands-on experience working with AI/ML or Generative AI systems is strongly preferred.
- Proven experience taking technical solutions from POC/prototype to production.
- You should be able to point to live platforms, products, or applications that you have built or significantly contributed to.
- An active GitHub profile with meaningful technical work is strongly preferred.
Team Management- Strong analytical and debugging skills with the ability to work through complex technical problems independently.
- Comfortable working with ambiguous requirements and translating them into practical technical solutions.
- Ability to work across AI/ML, software, infrastructure, and product layers.
- Strong communication skills and the ability to explain technical concepts to both engineering and non-technical stakeholders.
- Experience working directly with customers or customer engineering teams is a plus.
- Self-driven mindset with a strong bias towards building, experimenting, shipping, and improving.
Software Engineering- Strong programming skills in Python, with an emphasis on clean, maintainable, testable, and performant code.
- Strong understanding of data structures, algorithms, software design principles, and system design.
- Experience building and consuming REST APIs and integrating third-party systems.
- Strong understanding of databases, data pipelines, distributed systems, and application architecture.
- Experience with Git, testing, CI/CD, logging, monitoring, and observability.
- Strong hands-on experience with Docker and containerized applications.
- Ability to work with and debug existing production codebases.
AI/ML & GenAI- Strong hands-on understanding of Generative AI and LLM-based applications.
- Experience with RAG architectures, embeddings, vector databases, prompt engineering, and AI agents.
- Understanding of LLM evaluation, model benchmarking, inference, and performance optimization.
- Experience working with AI/ML frameworks and libraries.
- Experience with model fine-tuning or adaptation techniques is a plus.
- Understanding of AI application architecture, including retrieval, orchestration, model serving, and evaluation pipelines.
- Experience with GPU-based workloads and inference optimization is a plus.
- Experience with vllm, sglang, and sovereign AI deployments a big plus.
Cloud & Infrastructure- Proven experience deploying and operating applications on at least one major cloud platform — AWS, GCP, or Azure.
- Good understanding of cloud architecture, networking, compute, storage, IAM/security, and databases.
- Experience with container orchestration and production deployment environments.
- Understanding of scalability, reliability, performance, monitoring, and cloud cost optimization.
- Experience with GPU infrastructure and AI-specific cloud services is a strong plus.
Educational Background- Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, Statistics, or a related technical field is preferred.
- Strong practical experience and demonstrated technical work will be valued alongside formal qualifications.
What Success Looks LikeIn this role, you will be responsible for making sure our AI product works exceptionally well in real-world environments — from understanding the technical requirements and designing the right architecture to implementing integrations, deploying the system, solving production challenges, and feeding implementation learnings back into the product.
You will be successful if you can take ownership of complex technical problems, build production-grade AI systems, and consistently move implementations from idea to working product.
Click on Apply to know more.
