
AI/ML Engineer
5 days ago
Are you passionate about building intelligent systems that learn, adapt, and deliver real-world value? Join our high-impact AI & Machine Learning Engineering team and be a key contributor in shaping the next generation of intelligent applications. As an AI/ML Engineer, you'll have the unique opportunity to develop, deploy, and scale advanced ML and Generative AI (GenAI) solutions in production environments, leveraging cutting-edge technologies, frameworks, and cloud platforms.
In this role, you will collaborate with cross-functional teams including data engineers, product managers, MLOps engineers, and architects to design and implement production-grade AI solutions across domains. If you're looking to work at the intersection of deep learning, GenAI, cloud computing, and MLOps — this is the role for you.
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Design, develop, train, and deploy production-grade ML and GenAI models across use cases including NLP, computer vision, and structured data modeling.
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Leverage frameworks such as TensorFlow, Keras, PyTorch, and LangChain to build scalable deep learning and LLM-based solutions.
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Develop and maintain end-to-end ML pipelines with reusable, modular components for data ingestion, feature engineering, model training, and deployment.
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Implement and manage models on cloud platforms such as AWS, GCP, or Azure using services like SageMaker, Vertex AI, or Azure ML.
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Apply MLOps best practices using tools like MLflow, Kubeflow, Weights & Biases, Airflow, DVC, and Prefect to ensure scalable and reliable ML delivery.
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Incorporate CI/CD pipelines (using Jenkins, GitHub Actions, or similar) to automate testing, packaging, and deployment of ML workloads.
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Containerize applications using Docker and orchestrate scalable deployments via Kubernetes.
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Integrate LLMs with APIs and external systems using LangChain, Vector Databases (e.g., FAISS, Pinecone), and prompt engineering best practices.
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Collaborate closely with data engineers to access, prepare, and transform large-scale structured and unstructured datasets for ML pipelines.
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Build monitoring and retraining workflows to ensure models remain performant and robust in production.
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Evaluate and integrate third-party GenAI APIs or foundational models where appropriate to accelerate delivery.
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Maintain rigorous experiment tracking, hyperparameter tuning, and model versioning.
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Champion industry standards and evolving practices in ML lifecycle management, cloud-native AI architectures, and responsible AI.
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Work across global, multi-functional teams, including architects, principal engineers, and domain experts.
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4–7 years of hands-on experience in developing, training, and deploying ML/DL/GenAI models.
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Strong programming expertise in Python with proficiency in machine learning, data manipulation, and scripting.
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Demonstrated experience working with Generative AI models and Large Language Models (LLMs) such as GPT, LLaMA, Claude, or similar.
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Hands-on experience with deep learning frameworks like TensorFlow, Keras, or PyTorch.
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Experience in LangChain or similar frameworks for LLM-based app orchestration.
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Proven ability to implement and scale CI/CD pipelines for ML workflows using tools like Jenkins, GitHub, GitLab, or Bitbucket Pipelines.
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Familiarity with containerization (Docker) and orchestration tools like Kubernetes.
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Experience working with cloud platforms (AWS, Azure, GCP) and relevant AI/ML services such as SageMaker, Vertex AI, or Azure ML Studio.
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Knowledge of MLOps tools such as MLflow, Kubeflow, DVC, Weights & Biases, Airflow, and Prefect.
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Strong understanding of data engineering concepts, including batch/streaming pipelines, data lakes, and real-time processing (e.g., Kafka).
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Solid grasp of statistical modeling, machine learning algorithms, and evaluation metrics.
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Experience with version control systems (Git) and collaborative development workflows.
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Ability to translate complex business needs into scalable ML architectures and systems.
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Working knowledge of vector databases (e.g., FAISS, Pinecone, Weaviate) and semantic search implementation.
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Hands-on experience with prompt engineering, fine-tuning LLMs, or using techniques like LoRA, PEFT, RLHF.
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Familiarity with data governance, privacy, and responsible AI guidelines (bias detection, explainability, etc.).
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Certifications in AWS, Azure, GCP, or ML/AI specializations.
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Experience in high-compliance industries like pharma, banking, or healthcare.
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Familiarity with agile methodologies and working in iterative, sprint-based teams.
You will be a key member of an agile, forward-thinking AI/ML team that values curiosity, excellence, and impact. Our hybrid work culture promotes flexibility while encouraging regular in-person collaboration to foster innovation and team synergy. You'll have access to the latest technologies, mentorship, and continuous learning opportunities through hands-on projects and professional development resources.
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Build and deploy cutting-edge LLM and GenAI applications that solve real-world problems
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Collaborate with thought leaders across engineering, product, and data science
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Work in a dynamic, cloud-native, and automation-driven AI environment
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Accelerate your growth through certification programs and continuous learning
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Be part of an innovation-first team that values openness, agility, and integrity
About Agilisium -
- Agilisium, is an AWS technology Advanced Consulting Partner that enables companies to accelerate their "Data-to-Insights-Leap.
- With $25+ million in annual revenue and over 40% year-over-year growth, Agilisium is one of the fastest-growing IT solution providers in Southern California.
- Our most important asset? People.
- Talent management plays a vital role in our business strategy.
- We're looking for "drivers"; big thinkers with growth and strategic mindset — people who are committed to customer obsession, aren't afraid to experiment with new ideas.
- And we are all about finding and nurturing individuals who are ready to do great work.
- At Agilisium, you'll collaborate with great minds while being challenged to meet and exceed your potential
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