Innovate and build advanced AI/ML solutions for diverse industries, including BFSI, Energy & Utilities, Life Sciences, and Manufacturing. You will be instrumental in translating complex business needs into scalable machine learning architectures.

This role involves leading the entire ML project lifecycle, from initial data exploration and model creation to seamless deployment and ongoing monitoring. Ensuring alignment with organizational standards and best practices will be key to success.

Spearhead the design and development of robust AI/ML solutions across key industry verticals such as BFSI, Energy & Utilities, Life Sciences, and Manufacturing.

Collaborate closely with cross-functional teams to capture business requirements and engineer scalable machine learning architectures. You will own the end-to-end lifecycle of ML projects, encompassing data exploration, model development, deployment, and monitoring, always adhering to organizational standards.

Select optimal ML algorithms, frameworks, and tools to enhance model performance, scalability, and interpretability. Stay abreast of emerging ML techniques and technologies to drive continuous innovation. Define and implement effective data governance, model versioning, and deployment strategies for compliance and ethical AI use. Mentor junior team members to cultivate a collaborative and innovative environment. Effectively communicate technical solutions and project updates to stakeholders, translating complex ML concepts into accessible business language.

A Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, or a related field is essential.

Possess over 4 years of dedicated experience in AI/ML development, with a strong track record in solution design, model development, and deployment. Proven success in building ML solutions across various verticals, particularly BFSI, EnU, Life Sciences, and Manufacturing, is required.

Demonstrate high proficiency in programming languages like Python or R, coupled with hands-on experience in ML libraries such as Scikit-learn, XGBoost, TensorFlow, or PyTorch. A solid grasp of supervised, unsupervised, and reinforcement learning algorithms is necessary. Experience with cloud platforms like AWS, Azure, or GCP, including their respective ML services (e.g., SageMaker, Azure ML, Vertex AI), is crucial.

Exceptional analytical and problem-solving skills are paramount, as is the ability to thrive in dynamic, fast-paced environments. Excellent communication skills are needed to articulate technical concepts clearly to both technical and non-technical audiences.