Neysa - Machine Learning Solutions Architect (12-15 yrs)

Job title : Machine Learning Solutions Architect

Type : Full Time

Desired start date : Immediate

Minimum ualification : Any Graduate

Minimum Experience : 10-15 years in a relevant role

In this role, you will :

Lead the development and deployment of cutting-edge ML/AI solutions. This role requires a strong technical leader who can engage with business stakeholders and clients to define needs, design ML solutions, and oversee projects to deliver impactful results. The ideal candidate has expertise in ML infrastructure, including cloud platforms, GPUs, and DevOps practices, and can balance technical leadership with strategic client engagement.

Must-have skills :

Stakeholder C Client Management :

- Act as the primary liaison with business stakeholders and clients, ensuring alignment on project objectives and deliverables.

- Build and sustain client relationships, translating complex technical concepts into clear, accessible language.

- Work closely with clients to identify ML-driven solutions that address specific challenges.

- Define solution scope, KPIs, and ROI-focused strategies for ML solutions.

- Leverage data-driven insights to design practical, impactful solutions.

Team Leadership C Project Management :

- Lead and mentor a team of ML engineers and data scientists, fostering an innovative, growth-oriented environment.

- Oversee project timelines, resources, and deliverables to ensure high- quality outcomes.

- Guide the team through technical challenges, facilitating problem-solving and professional development.

Machine Learning C AI Solution Development :

- Architect and deploy ML/AI solutions using advanced ML algorithms, such as deep learning and transformer models.

- Design scalable, efficient models optimized for low-latency performance in production environments.

- Leverage GPU computing to accelerate workloads, focusing on cost- effective and high-performance outcomes.

Added skills you may need to have :

- GPU Computing G Optimization : Expertise in GPU acceleration, including CUDA libraries for optimized model training and inference.

- Model Deployment G Inference Servers : Proficiency in deploying ML models with tools like NVIDIA Triton or TensorFlow Serving, with an emphasis on high- throughput performance.

- Cloud Platform Integration : Experience with AWS, GCP, or Azure for scalable ML infrastructure, including familiarity with tools like AWS SageMaker and Google AI Platform.

- DevOps Knowledge : Skilled in CI/CD for ML, containerization (Docker), orchestration (Kubernetes), and IaC tools (Terraform, AWS CloudFormation) to streamline ML workflows.

- Data Engineering G MLOps : Proficient in MLOps tools like MLflow, DVC, or Kubeflow, with experience building reliable data pipelines and working with large datasets.

What can you expect A working environment like no other :

- The best equipment which complements your talents

- The best tools in the business for you to bring your creations to life

- A great environment

- Flexible work hours, and flexible work locations

- The opportunity to make your mark and shape the future

The Skill :

Things you must know :

- MS/BS degree in Computer Science, Engineering or equivalent preferred

- Exceptional interpersonal and communication skills with a proven record of managing complex stakeholder relationships.

- 12 to 15 years in machine learning, data science, or AI, with at least 5 years in a leadership or client- facing role.

- In-depth expertise in ML and AI frameworks (e.g., TensorFlow, PyTorch), GPU computing, cloud deployment, and model optimization.

- Strong team leadership and mentoring abilities, with a focus on fostering innovation and growth.