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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.
Design, develop, train, and deploy production-grade ML and GenAI models across use cases including NLP, computer vision, and structured data modeling.
Leverage frameworks such as TensorFlow, Keras, PyTorch, and LangChain to build scalable deep learning and LLM-based solutions.
Develop and maintain end-to-end ML pipelines with reusable, modular components for data ingestion, feature engineering, model training, and deployment.
Implement and manage models on cloud platforms such as AWS, GCP, or Azure using services like SageMaker, Vertex AI, or Azure ML.
Apply MLOps best practices using tools like MLflow, Kubeflow, Weights & Biases, Airflow, DVC, and Prefect to ensure scalable and reliable ML delivery.
Incorporate CI/CD pipelines (using Jenkins, GitHub Actions, or similar) to automate testing, packaging, and deployment of ML workloads.
Containerize applications using Docker and orchestrate scalable deployments via Kubernetes.
Integrate LLMs with APIs and external systems using LangChain, Vector Databases (e.g., FAISS, Pinecone), and prompt engineering best practices.
Collaborate closely with data engineers to access, prepare, and transform large-scale structured and unstructured datasets for ML pipelines.
Build monitoring and retraining workflows to ensure models remain performant and robust in production.
Evaluate and integrate third-party GenAI APIs or foundational models where appropriate to accelerate delivery.
Maintain rigorous experiment tracking, hyperparameter tuning, and model versioning.
Champion industry standards and evolving practices in ML lifecycle management, cloud-native AI architectures, and responsible AI.
Work across global, multi-functional teams, including architects, principal engineers, and domain experts.
4–7 years of hands-on experience in developing, training, and deploying ML/DL/GenAI models.
Strong programming expertise in Python with proficiency in machine learning, data manipulation, and scripting.
Demonstrated experience working with Generative AI models and Large Language Models (LLMs) such as GPT, LLaMA, Claude, or similar.
Hands-on experience with deep learning frameworks like TensorFlow, Keras, or PyTorch.
Experience in LangChain or similar frameworks for LLM-based app orchestration.
Proven ability to implement and scale CI/CD pipelines for ML workflows using tools like Jenkins, GitHub, GitLab, or Bitbucket Pipelines.
Familiarity with containerization (Docker) and orchestration tools like Kubernetes.
Experience working with cloud platforms (AWS, Azure, GCP) and relevant AI/ML services such as SageMaker, Vertex AI, or Azure ML Studio.
Knowledge of MLOps tools such as MLflow, Kubeflow, DVC, Weights & Biases, Airflow, and Prefect.
Strong understanding of data engineering concepts, including batch/streaming pipelines, data lakes, and real-time processing (e.g., Kafka).
Solid grasp of statistical modeling, machine learning algorithms, and evaluation metrics.
Experience with version control systems (Git) and collaborative development workflows.
Ability to translate complex business needs into scalable ML architectures and systems.
Working knowledge of vector databases (e.g., FAISS, Pinecone, Weaviate) and semantic search implementation.
Hands-on experience with prompt engineering, fine-tuning LLMs, or using techniques like LoRA, PEFT, RLHF.
Familiarity with data governance, privacy, and responsible AI guidelines (bias detection, explainability, etc.).
Certifications in AWS, Azure, GCP, or ML/AI specializations.
Experience in high-compliance industries like pharma, banking, or healthcare.
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.
Build and deploy cutting-edge LLM and GenAI applications that solve real-world problems
Collaborate with thought leaders across engineering, product, and data science
Work in a dynamic, cloud-native, and automation-driven AI environment
Accelerate your growth through certification programs and continuous learning
Be part of an innovation-first team that values openness, agility, and integrity
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