AL/ML Technical Architect
Tata Consultancy Services
•
New York, NY
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0
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Not Applicable
Posted April 2, 2026
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Responsibilities
Responsibilities
- Architect and drive the design, development, and deployment of scalable ML/AI solutions.
- Lead and actively contribute to data science initiatives across the full project lifecycle – from
- ideation and solution design to hands-on development, deployment, and production support.
- Define standards, best practices, and governance for AI/ML solutioning and model management.
- Collaborate with data engineering, MLOps, product, and business teams.
- Oversee integration of data science models into production systems.
- Design and implement AI solutions on cloud platforms (AWS/Azure/GCP) and/or on-prem using
- Evaluate and recommend ML tools, frameworks, and cloud-native services aligned to performance,
- security, and cost goals.
- Ensure architectures address enterprise non-functionals: scalability, resiliency, observability, security,
- and compliance.
- Perform MLOps design and implementation (and lead the team on same if needed) including CI/CD for ML,
- reproducibility, model registry, monitoring, drift detection, and operational controls
- Define standards, best practices, and governance for AI/ML solutioning and model management
- (validation, documentation, approvals, audits).
- Architect solutions leveraging LLMs (including GPT-class models) for enterprise GenAI use cases and design
- patterns such as RAG, guardrails, evaluation and safety controls.
- Guide data strategy and feature store design; align data engineering and ML engineering for reliable feature
- pipelines.
- Provide architectural direction for big data processing using Spark/PySpark for large-scale feature generation
- and training workflows.
- Partner with stakeholders to identify high-value use cases, shape roadmaps, and translate business requirements
- into robust technical architecture
- Present architecture, trade-offs, risk controls, and delivery plans clearly to technical and executive audiences.
- Mentor ML engineers, data scientists, and platform teams; establish architectural guardrails, design reviews,
- and engineering standards.
- and mentor cross-functional teams (data scientists, ML engineers, data engineers, and application teams).
- closely with stakeholders to identify high-value use cases and ensure seamless integration of models into
Not Met Priorities
What still needs stronger evidence
Requirements
- significant depth in AI/ML architecture and solution design on customer-facing programs
- Strong hands-on understanding of ML development frameworks and ecosystems
- (e.g., Python + standard ML/DL libraries like scikit-learn, TensorFlow, PyTorch, HuggingFace)
- Proven architecture experience for cloud-native AI/ML solutions (AWS/Azure/GCP) and production deployments
- Strong MLOps experience: model lifecycle, governance, monitoring, and production controls.
- Deep GenAI/LLM architecture experience (RAG patterns, evaluation harnesses, prompt orchestration, guardrails)
- Graph/Network analytics exposure (Neo4j/TigerGraph/NetworkX/GraphX).
- Spark/Scala/PySpark at enterprise scale (data processing + ML pipelines)
- BFSI domain exposure and ability to design within regulated / compliance-heavy environments.
- Design and implement AI solutions on cloud platforms (AWS/Azure/GCP) and/or on-prem using
- open-source technologies
- Evaluate and recommend ML tools, frameworks, and cloud-native services aligned to performance,
- Perform MLOps design and implementation (and lead the team on same if needed) including CI/CD for ML,
- reproducibility, model registry, monitoring, drift detection, and operational controls
- Define standards, best practices, and governance for AI/ML solutioning and model management
- (validation, documentation, approvals, audits).
- Architect solutions leveraging LLMs (including GPT-class models) for enterprise GenAI use cases and design
- pipelines.
- Provide architectural direction for big data processing using Spark/PySpark for large-scale feature generation
- Mentor ML engineers, data scientists, and platform teams; establish architectural guardrails, design reviews,
- and engineering standards.
- Open-source contributions or published research.
- Generic Managerial Skills, If any
- A senior AI/ML Architect (15+ years) to perform end-to-end architecture and delivery of scalable, secure,
- production-grade AI/ML systems for BFSI clients.
- You will own solution blueprints across the full lifecycle—from
- use-case discovery and architecture to platform selection, MLOps/LLMOps design, deployment, and governance—
- and mentor cross-functional teams (data scientists, ML engineers, data engineers, and application teams).
- Your deep expertise in machine learning, cloud-native architectures, MLOps practices, and
Preferred Skills
- (e.g., Python + standard ML/DL libraries like scikit-learn, TensorFlow, PyTorch, HuggingFace)
- BFSI domain exposure and ability to design within regulated / compliance-heavy environments.
Education
- (Not required) – Qualifications: BACHELOR OF COMPUTER SCIENCE
Job Description
Must Have Technical/Functional Skills
significant depth in AI/ML architecture and solution design on customer-facing programs
Strong hands-on understanding of ML development frameworks and ecosystems
(e.g., Python + standard ML/DL libraries like scikit-learn, TensorFlow, PyTorch, HuggingFace)
Proven architecture experience for cloud-native AI/ML solutions (AWS/Azure/GCP) and production deployments
Strong MLOps experience: model lifecycle, governance, monitoring, and production controls.
Deep GenAI/LLM architecture experience (RAG patterns, evaluation harnesses, prompt orchestration, guardrails)
Graph/Network analytics exposure (Neo4j/TigerGraph/NetworkX/GraphX).
Spark/Scala/PySpark at enterprise scale (data processing + ML pipelines)
BFSI domain exposure and ability to design within regulated / compliance-heavy environments.
Roles & Responsibilities
Architect and drive the design, development, and deployment of scalable ML/AI solutions.
Lead and actively contribute to data science initiatives across the full project lifecycle – from
ideation and solution design to hands-on development, deployment, and production support.
Define standards, best practices, and governance for AI/ML solutioning and model management.
Collaborate with data engineering, MLOps, product, and business teams.
Oversee integration of data science models into production systems.
Design and implement AI solutions on cloud platforms (AWS/Azure/GCP) and/or on-prem using
open-source technologies
Evaluate and recommend ML tools, frameworks, and cloud-native services aligned to performance,
security, and cost goals.
Ensure architectures address enterprise non-functionals: scalability, resiliency, observability, security,
and compliance.
Perform MLOps design and implementation (and lead the team on same if needed) including CI/CD for ML,
reproducibility, model registry, monitoring, drift detection, and operational controls
Define standards, best practices, and governance for AI/ML solutioning and model management
(validation, documentation, approvals, audits).
Architect solutions leveraging LLMs (including GPT-class models) for enterprise GenAI use cases and design
patterns such as RAG, guardrails, evaluation and safety controls.
Guide data strategy and feature store design; align data engineering and ML engineering for reliable feature
pipelines.
Provide architectural direction for big data processing using Spark/PySpark for large-scale feature generation
and training workflows.
Partner with stakeholders to identify high-value use cases, shape roadmaps, and translate business requirements
into robust technical architecture
Present architecture, trade-offs, risk controls, and delivery plans clearly to technical and executive audiences.
Mentor ML engineers, data scientists, and platform teams; establish architectural guardrails, design reviews,
and engineering standards.
Open-source contributions or published research.
Generic Managerial Skills, If any
A senior AI/ML Architect (15+ years) to perform end-to-end architecture and delivery of scalable, secure,
production-grade AI/ML systems for BFSI clients. You will own solution blueprints across the full lifecycle—from
use-case discovery and architecture to platform selection, MLOps/LLMOps design, deployment, and governance—
and mentor cross-functional teams (data scientists, ML engineers, data engineers, and application teams). You will work
closely with stakeholders to identify high-value use cases and ensure seamless integration of models into
business applications. Your deep expertise in machine learning, cloud-native architectures, MLOps practices, and
financial domain knowledge will be essential to influence strategy and deliver transformative business impact
Salary Range: $180,000 to $210,000 per year
Qualifications: BACHELOR OF COMPUTER SCIENCE
Must Have Technical/Functional Skills
significant depth in AI/ML architecture and solution design on customer-facing programs
Strong hands-on understanding of ML development frameworks and ecosystems
(e.g., Python + standard ML/DL libraries like scikit-learn, TensorFlow, PyTorch, HuggingFace)
Proven architecture experience for cloud-native AI/ML solutions (AWS/Azure/GCP) and production deployments
Strong MLOps experience: model lifecycle, governance, monitoring, and production controls.
Deep GenAI/LLM architecture experience (RAG patterns, evaluation harnesses, prompt orchestration, guardrails)
Graph/Network analytics exposure (Neo4j/TigerGraph/NetworkX/GraphX).
Spark/Scala/PySpark at enterprise scale (data processing + ML pipelines)
BFSI domain exposure and ability to design within regulated / compliance-heavy environments.
Roles & Responsibilities
Architect and drive the design, development, and deployment of scalable ML/AI solutions.
Lead and actively contribute to data science initiatives across the full project lifecycle – from
ideation and solution design to hands-on development, deployment, and production support.
Define standards, best practices, and governance for AI/ML solutioning and model management.
Collaborate with data engineering, MLOps, product, and business teams.
Oversee integration of data science models into production systems.
Design and implement AI solutions on cloud platforms (AWS/Azure/GCP) and/or on-prem using
open-source technologies
Evaluate and recommend ML tools, frameworks, and cloud-native services aligned to performance,
security, and cost goals.
Ensure architectures address enterprise non-functionals: scalability, resiliency, observability, security,
and compliance.
Perform MLOps design and implementation (and lead the team on same if needed) including CI/CD for ML,
reproducibility, model registry, monitoring, drift detection, and operational controls
Define standards, best practices, and governance for AI/ML solutioning and model management
(validation, documentation, approvals, audits).
Architect solutions leveraging LLMs (including GPT-class models) for enterprise GenAI use cases and design
patterns such as RAG, guardrails, evaluation and safety controls.
Guide data strategy and feature store design; align data engineering and ML engineering for reliable feature
pipelines.
Provide architectural direction for big data processing using Spark/PySpark for large-scale feature generation
and training workflows.
Partner with stakeholders to identify high-value use cases, shape roadmaps, and translate business requirements
into robust technical architecture
Present architecture, trade-offs, risk controls, and delivery plans clearly to technical and executive audiences.
Mentor ML engineers, data scientists, and platform teams; establish architectural guardrails, design reviews,
and engineering standards.
Open-source contributions or published research.
Generic Managerial Skills, If any
A senior AI/ML Architect (15+ years) to perform end-to-end architecture and delivery of scalable, secure,
production-grade AI/ML systems for BFSI clients. You will own solution blueprints across the full lifecycle—from
use-case discovery and architecture to platform selection, MLOps/LLMOps design, deployment, and governance—
and mentor cross-functional teams (data scientists, ML engineers, data engineers, and application teams). You will work
closely with stakeholders to identify high-value use cases and ensure seamless integration of models into
business applications. Your deep expertise in machine learning, cloud-native architectures, MLOps practices, and
financial domain knowledge will be essential to influence strategy and deliver transformative business impact
Salary Range: $180,000 to $210,000 per year
Qualifications: BACHELOR OF COMPUTER SCIENCE