AI & MLops EngineerGCP Infra
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Job Summary
14 years of experience in cloud infrastructure engineering DevOps or platform engineering.
Experience with GenAI use cases (chatbots content generation code assistants etc.).
Strong hands-on expertise with Google Cloud Platform (GCP) especially Vertex AI.
Experience with IBM Watsonx for AI application deployment and management.
Proven skills in Docker Kubernetes (GKE) and container orchestration at scale.
Proficiency in Python Bash or other relevant scripting languages.
Strong understanding of cloud networking IAM and security best practices.
Experience with CI/CD tools (GitHub Actions GitLab CI Jenkins) and IaC tools (Terraform Pulumi Ansible Deployment Manager).
Familiarity with data pipelines and integration tools (Dataflow Apache Beam Pub/Sub Kafka).
Excellent problem-solving debugging and communication skills.
Preferred Experience
Experience in MLOps practices for model deployment monitoring and retraining.
Exposure to multi-cloud or hybrid cloud environments (GCP AWS Azure on-prem).
Hands-on experience with feature stores (Vertex AI Feature Store Feast) and ML observability tools (EvidentlyAI Fiddler).
Knowledge of distributed training frameworks (Horovod DeepSpeed PyTorch Distributed).
Contributions to open-source projects in infrastructure MLOps or GenAI.
Experience managing infrastructure in regulated industries.
Experience:
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Minimum 10 years of experience in applied data science machine learning generative AI or advanced analytics.
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Proven experience in building and launching moderate-to-large-scale analytics and AI projects into production.
Technical Skills:
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Proficiency in Python R and SQL for data preparation querying and model development.
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Strong knowledge of supervised unsupervised and generative AI techniques such as regression classification clustering causal inference and large language models (LLMs).
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Hands-on experience with GCP Vertex AI IBM WatsonX Databricks or SageMaker and frameworks like TensorFlow PyTorch and Keras.
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Familiarity with data visualization tools (e.g. Tableau Power BI Shiny D3) to communicate insights effectively.
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Experience working with Linux/Unix and Windows environments.
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Familiarity with Java or C is a plus.
Professional Skills:
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Strong analytical skills with attention to detail and a rigorous problem-solving approach.
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Ability to translate complex business problems into high-level AI and analytics solutions.
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Excellent oral and written communication skills with the ability to explain analytical and generative AI concepts to both technical and non-technical stakeholders.
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Strong storytelling skills to communicate data-driven insights in a clear impactful way.
Preferred Experience
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Expertise in cloud AI technologies (GCP IBM WatsonX AWS Azure) and modern data pipelines.
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Demonstrated success in implementing generative AI (LLMs text-to-image summarization conversational AI) for business use cases.
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Track record of curiosity and innovation with the ability to explore complex datasets and generate actionable insights.
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Background in operations research or quantitative social science is a strong plus.
