Technical Architect - ML
About the job
- At least 12 years of software engineering experience, including at least 6 years designing and deploying production machine-learning or artificial-intelligence systems at scale.
- Expert-level proficiency in Python and machine-learning frameworks such as PyTorch, TensorFlow, or JAX.
- Hands-on experience building and deploying large language models, including fine-tuning, instruction tuning, reinforcement learning from human feedback, quantization, LoRA/QLoRA, and inference optimization.
- Deep knowledge of MLOps tools and platforms such as Kubeflow, MLflow, Airflow, Dagster, Flyte, Metaflow, or ZenML.
- Strong understanding of distributed systems, microservices, containerization with Docker, orchestration with Kubernetes, and infrastructure as code with Terraform or Pulumi.
- Experience with vector databases such as Pinecone, Weaviate, Milvus, or Qdrant, and retrieval-augmented generation architectures.
- A proven track record designing feature stores such as Feast or Tecton, online and offline inference systems, and model-monitoring solutions.
- Expertise in cloud platforms such as AWS, GCP, or Azure and their managed machine-learning services.
- Ability to lead cross-functional technical teams and influence architecture decisions at the executive level.
- Ability to explain complex machine-learning concepts to non-technical stakeholders.
- Experience defining artificial-intelligence roadmaps and presenting to C-level executives.
- Define and own end-to-end technical architecture for AI/ML platforms and products, from data ingestion through model serving and monitoring.
- Lead the design of scalable machine-learning pipelines, MLOps frameworks, and generative AI or large-language-model systems.
- Architect cloud-native AI solutions using AWS SageMaker, GCP Vertex AI, Azure ML, or multi-cloud setups.
- Evaluate and select appropriate algorithms, frameworks, and tools, including PyTorch, TensorFlow, JAX, LangChain, LlamaIndex, Ray, Kubeflow, and MLflow.
- Design systems for large-scale model training and inference, including distributed training, model parallelism, quantization, and efficient serving with Triton, vLLM, or TGI.
- Establish Responsible AI practices for fairness, explainability using SHAP or LIME, bias mitigation, privacy through federated learning or differential privacy, and security.
- Build and govern enterprise MLOps platforms, including feature stores, model registries, CI/CD for machine learning, experiment tracking, and observability.
- Collaborate with data engineers, ML engineers, software engineers, and product teams to translate business requirements into robust technical solutions.
- Drive proofs of concept and spike solutions for emerging technologies, including large language models, multimodal models, agentic systems, and retrieval-augmented generation.
- Mentor senior ML engineers and architects, set technical standards, and conduct architecture reviews.
- Monitor current research and productionize emerging techniques when they offer clear business value.
- Contributions to open-source machine-learning projects.
- Knowledge of enterprise data platforms such as Snowflake, Databricks, or BigQuery.
About the company
Prodapt focuses on the telecommunications, media, and technology sector (the 'Connectedness' industry) by providing specialized consulting and managed services. It helps clients through a service-based model that includes strategic consulting, application development, systems integration, and ongoing operational support. Its offerings cover digital transformation, cloud and network services, fiber rollout, 5G enablement, cloud migration, and applying AI to network operations to modernize telecom operators and digital platform companies. What sets Prodapt apart is its deep, industry-specific focus on the Connectedness vertical, allowing it to build tailored frameworks and solutions rather than generic IT services, backed by global reach across the Americas, Europe, India, and Africa. The company aims to be a strategic partner that helps clients navigate the evolving digital landscape, accelerate deployment of new services, and optimize network operations.
Company Size
5,001-10,000
Company Stage
Growth Equity (Venture Capital)
Total Funding
$75M
Headquarters
India
Founded
1999
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Simplify's Take
What believers are saying
- OpenAI Select Partner status should accelerate enterprise AI deal flow in 2026.
- ISO 42001 certification strengthens sales into regulated telecom and media accounts.
- Panama expansion and Richardson innovation center improve nearshore execution for Americas growth.
What critics are saying
- Prodapt's workforce fell 8.5% from 2023 to March 2026, signaling pressure.
- Telecom client spending remains cyclical; losing one CSP could quickly hit utilization.
- OpenAI partnership commoditizes delivery unless Prodapt wins proprietary telecom AI products.
What makes Prodapt unique
- OpenAI Select Partner in August 2026, validating its telecom-native AI services brand.
- OneFibreData on Snowflake targets fiber operators with domain-specific data workflows.
- Panama and Dallas centers deepen Americas delivery for CSP clients.
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Benefits
- Remote Work Options