What are the responsibilities and job description for the Senior AI/ML Engineer position at Jobs via Dice?
Dice is the leading career destination for tech experts at every stage of their careers. Our client, Innovatix Technology Partners, is seeking the following. Apply via Dice today!
Mandatory Skills: AI/ML, Python/Java/GO, AWS
Job Description:
We are seeking a highly motivated AI Engineer with a strong background in software engineering and hands-on experience in applied AI/ML to support AI Transformation initiatives. The ideal candidate will have 1-3 years of experience building, integrating, or deploying AI-driven solutions and be proficient in at least one modern programming language (GO, Python, Java, C#) and exposure to DevOps practices & Cloud environment.
You will work closely with cross functional teams to design, develop, and operationalize AI solutions that enhance automation, improve decision-making, and drive business value.
Required Skills & Qualifications
1-3 years of experience in AI/ML engineering or related roles
Strong programming skills in one or more programming languages: GO/Java/Python/C#
Good understanding of machine learning concepts and lifecycle
Hands-on experience with ML frameworks/libraries
Exposure to DevOps practices (CI/CD, automation, environment management)
Experience with tools like Git, Jenkins, GitHub Actions, or similar
Familiarity with containerization (Docker) and basics of orchestration (Kubernetes)
Understanding of REST APIs and microservices architecture
Exposure to cloud platforms (AWS, Azure)
Good understanding of SDLC process
Experience working in Agile environments
Strong analytical and problem-solving abilities
Good communication and stakeholder collaboration skills
Ability to work in fast-paced, evolving environments
Continuous learning mindset
Preferred Qualifications:
Experience with MLOps tools/platforms
Exposure to Generative AI / LLMs (prompt engineering, embeddings, fine-tuning)
Familiarity with infrastructure as code (Terraform, CloudFormation)
Experience with monitoring/logging tools (Prometheus, Grafana, ELK stack)
Knowledge of data pipelines and streaming tools (Kafka, Airflow)
Key Responsibilities:
Design, develop, and deploy AI/ML models and intelligent applications
Contribute to AI transformation initiatives, including automation and generative AI use cases
Build and maintain end-to-end ML pipelines (data ingestion, training, validation, deployment)
Evaluate and improve model performance through experimentation and tuning
Integrate AI models into enterprise applications, APIs, and workflows
Deploy models into production using CI/CD pipelines
Collaborate with DevOps and platform teams to ensure scalability, monitoring, and reliability
Collaborate with engineers, QA s to identify areas of automation in documentation, software code development, test case generation and validation
Develop and expose AI services via APIs and microservices
Monitor model performance and implement retraining/optimization strategies
Ensure proper versioning of models, datasets, and code
Participate in system design, code reviews, and release processes
Contribute to AI adoption strategies, proof-of-concepts (POCs), and pilots
Work with minimal support or handholding
Mandatory Skills: AI/ML, Python/Java/GO, AWS
Job Description:
We are seeking a highly motivated AI Engineer with a strong background in software engineering and hands-on experience in applied AI/ML to support AI Transformation initiatives. The ideal candidate will have 1-3 years of experience building, integrating, or deploying AI-driven solutions and be proficient in at least one modern programming language (GO, Python, Java, C#) and exposure to DevOps practices & Cloud environment.
You will work closely with cross functional teams to design, develop, and operationalize AI solutions that enhance automation, improve decision-making, and drive business value.
Required Skills & Qualifications
1-3 years of experience in AI/ML engineering or related roles
Strong programming skills in one or more programming languages: GO/Java/Python/C#
Good understanding of machine learning concepts and lifecycle
Hands-on experience with ML frameworks/libraries
Exposure to DevOps practices (CI/CD, automation, environment management)
Experience with tools like Git, Jenkins, GitHub Actions, or similar
Familiarity with containerization (Docker) and basics of orchestration (Kubernetes)
Understanding of REST APIs and microservices architecture
Exposure to cloud platforms (AWS, Azure)
Good understanding of SDLC process
Experience working in Agile environments
Strong analytical and problem-solving abilities
Good communication and stakeholder collaboration skills
Ability to work in fast-paced, evolving environments
Continuous learning mindset
Preferred Qualifications:
Experience with MLOps tools/platforms
Exposure to Generative AI / LLMs (prompt engineering, embeddings, fine-tuning)
Familiarity with infrastructure as code (Terraform, CloudFormation)
Experience with monitoring/logging tools (Prometheus, Grafana, ELK stack)
Knowledge of data pipelines and streaming tools (Kafka, Airflow)
Key Responsibilities:
Design, develop, and deploy AI/ML models and intelligent applications
Contribute to AI transformation initiatives, including automation and generative AI use cases
Build and maintain end-to-end ML pipelines (data ingestion, training, validation, deployment)
Evaluate and improve model performance through experimentation and tuning
Integrate AI models into enterprise applications, APIs, and workflows
Deploy models into production using CI/CD pipelines
Collaborate with DevOps and platform teams to ensure scalability, monitoring, and reliability
Collaborate with engineers, QA s to identify areas of automation in documentation, software code development, test case generation and validation
Develop and expose AI services via APIs and microservices
Monitor model performance and implement retraining/optimization strategies
Ensure proper versioning of models, datasets, and code
Participate in system design, code reviews, and release processes
Contribute to AI adoption strategies, proof-of-concepts (POCs), and pilots
Work with minimal support or handholding