ML Ops / Agent Ops Engineer
Top Benefits
About the role
Location: Toronto – Hybrid at least 3 days in office
Experience: 12+ years
Please note, this role is not able to offer visa transfer or sponsorship now or in the future*
Job Description
We are seeking a highly skilled ML Ops / Agent Ops Engineer to design, build, and manage scalable AI-driven systems leveraging large language models (LLMs) and agent-based architectures. This role focuses on developing intelligent agents, implementing robust RAG pipelines, and ensuring production-grade deployment of AI solutions.
The ideal candidate will have strong expertise in Python, LLM APIs (OpenAI/Anthropic), agent orchestration frameworks, and MLOps/AgentOps practices.
Key Responsibilities
Design and implement AI agents using frameworks such as LangGraph, CrewAI, AutoGen, or LangChain Build and optimize Retrieval-Augmented Generation (RAG) pipelines for knowledge retrieval and contextual reasoning Develop Python-based API clients for integrating LLM services Work with Anthropic Claude API and/or OpenAI Agents SDK, including tool use and system prompts Implement prompt engineering strategies, including versioning and evaluation Establish and manage MLOps/AgentOps pipelines for deployment and monitoring Integrate and manage vector databases (e.g., FAISS, Pinecone, Weaviate, Chroma) Enable system monitoring through observability tools (e.g., LangSmith, Datadog, Weights & Biases) Ensure high performance, scalability, and reliability of AI-powered applications
Required Qualifications
Strong proficiency in Python, including async programming and API development Hands-on experience with OpenAI APIs and/or Anthropic Claude API (Agent SDK preferred) Experience with at least one agent orchestration framework: LangGraph CrewAI AutoGen LangChain Experience building RAG pipelines and working with vector databases Familiarity with prompt engineering, versioning, and evaluation frameworks Understanding of MLOps or AgentOps practices, including CI/CD pipelines and monitoring Key Skills
Python Programming LLM APIs (OpenAI, Anthropic Claude) LangChain / LangGraph / CrewAI / AutoGen RAG Pipelines Vector Databases Prompt Engineering & Evaluation MLOps / AgentOps Observability & Monitoring
This position is also eligible for Cognizant’s discretionary annual incentive program, based on performance and subject to the terms of Cognizant’s applicable plans.
Compensation: we are offering an annual salary between $69,750-$110,000
Disclaimer: The salary, other compensation, and benefits information is accurate as of the date of this posting. Cognizant reserves the right to modify this information at any time, subject to applicable law.
At Cognizant, we're eager to meet people who believe in our mission and can make an impact in various ways! We strongly encourage you to apply even if you only meet the required skills listed. Consider what transferrable experience and skills make you an outstanding applicant and help us see how you'd be helpful to this role.
Cognizant will only consider applicants for this position who are legally authorized to work in Canada without requiring employer sponsorship, now or at any time in the future.
At Cognizant, we strive to provide flexibility wherever possible, and we are here to support a healthy work-life balance though our various wellbeing programs.
Note: The working arrangements for this role are accurate as of the date of posting. This may change based on the project you’re engaged in, as well as business and client requirements. Rest assured; we will always be clear about role expectations.
#CB
#IND123
Similar Jobs
ML Ops / Agent Ops Engineer
Top Benefits
About the role
Location: Toronto – Hybrid at least 3 days in office
Experience: 12+ years
Please note, this role is not able to offer visa transfer or sponsorship now or in the future*
Job Description
We are seeking a highly skilled ML Ops / Agent Ops Engineer to design, build, and manage scalable AI-driven systems leveraging large language models (LLMs) and agent-based architectures. This role focuses on developing intelligent agents, implementing robust RAG pipelines, and ensuring production-grade deployment of AI solutions.
The ideal candidate will have strong expertise in Python, LLM APIs (OpenAI/Anthropic), agent orchestration frameworks, and MLOps/AgentOps practices.
Key Responsibilities
Design and implement AI agents using frameworks such as LangGraph, CrewAI, AutoGen, or LangChain Build and optimize Retrieval-Augmented Generation (RAG) pipelines for knowledge retrieval and contextual reasoning Develop Python-based API clients for integrating LLM services Work with Anthropic Claude API and/or OpenAI Agents SDK, including tool use and system prompts Implement prompt engineering strategies, including versioning and evaluation Establish and manage MLOps/AgentOps pipelines for deployment and monitoring Integrate and manage vector databases (e.g., FAISS, Pinecone, Weaviate, Chroma) Enable system monitoring through observability tools (e.g., LangSmith, Datadog, Weights & Biases) Ensure high performance, scalability, and reliability of AI-powered applications
Required Qualifications
Strong proficiency in Python, including async programming and API development Hands-on experience with OpenAI APIs and/or Anthropic Claude API (Agent SDK preferred) Experience with at least one agent orchestration framework: LangGraph CrewAI AutoGen LangChain Experience building RAG pipelines and working with vector databases Familiarity with prompt engineering, versioning, and evaluation frameworks Understanding of MLOps or AgentOps practices, including CI/CD pipelines and monitoring Key Skills
Python Programming LLM APIs (OpenAI, Anthropic Claude) LangChain / LangGraph / CrewAI / AutoGen RAG Pipelines Vector Databases Prompt Engineering & Evaluation MLOps / AgentOps Observability & Monitoring
This position is also eligible for Cognizant’s discretionary annual incentive program, based on performance and subject to the terms of Cognizant’s applicable plans.
Compensation: we are offering an annual salary between $69,750-$110,000
Disclaimer: The salary, other compensation, and benefits information is accurate as of the date of this posting. Cognizant reserves the right to modify this information at any time, subject to applicable law.
At Cognizant, we're eager to meet people who believe in our mission and can make an impact in various ways! We strongly encourage you to apply even if you only meet the required skills listed. Consider what transferrable experience and skills make you an outstanding applicant and help us see how you'd be helpful to this role.
Cognizant will only consider applicants for this position who are legally authorized to work in Canada without requiring employer sponsorship, now or at any time in the future.
At Cognizant, we strive to provide flexibility wherever possible, and we are here to support a healthy work-life balance though our various wellbeing programs.
Note: The working arrangements for this role are accurate as of the date of posting. This may change based on the project you’re engaged in, as well as business and client requirements. Rest assured; we will always be clear about role expectations.
#CB
#IND123