Machine Learning Engineer

Introduction DeCentralCode:

DeCentralCode is a technology company that applies modern solutions to address current challenges. With locations in Rotterdam, Netherlands and Salem, India, we operate on a global scale.

Our expertise lies in cutting-edge technologies such as machine learning/artificial intelligence, distributed ledger technology, blockchain, Internet of Things, cloud-native, serverless architecture, and cybersecurity.

We collaborate with knowledge institutions, governments, and businesses to co-create and validate solutions for various domains including supply chain and energy transition, among others.

As an early-stage partner, we offer the opportunity to work with the cutting-edge technologies from scratch.

Location:

Salem, Tamil Nadu, India.

We are looking for:

As a Machine Learning Engineer, you will be responsible for designing, developing, and implementing machine learning algorithms and models to solve complex business problems and enhance our products and services. You will collaborate closely with cross-functional teams, including data scientists, software engineers, and product managers, to drive the development and deployment of machine learning solutions.

• Ability to identify and solve complex problems using machine learning techniques, with a focus on delivering actionable insights and tangible business value.
• Implement best practices for testing and validation of machine learning models, ensuring robustness, reliability, and reproducibility of results.
• Effectively communicate technical concepts and insights to non-technical stakeholders, and present findings and recommendations in a clear and concise manner.

Responsibilities:

• Understanding the functional aspects of the clients requirement.
• Ability to work in an Agile environment at scale.
• Ability to work independently and as part of a team.
• Efficient Task Execution and adherence to schedules, timelines.
• Raise clarifications, issues, concerns regarding work output to the lead on time.
• Provide periodic task level updates as required.
• Ensure appropriate classification and prioritization of tasks.
• Comply with defined process during task execution.
• Assisting in creating documentations about client specific applications project specific processes and workflows.
• Adhere to company policies and procedures.
• Participation In Calls, Status Reporting
• Participating in daily stand-up calls and weekly calls.
• Attend any Knowledge Transition sessions conducted within the project to gain proficiency in project level knowledge.
• Understanding details of the quality processes and practices followed in the project from different sources.
• Completing the domain, technical, project relation training and certifications.

Necessary Skills:

• Experience: 3-5 years of experience in machine learning within a production environment. Proven track record of productionizing ML models at scale.
• Programming Languages and Libraries:?Proficiency in Python. Experience with analytical packages/libraries such as Pandas, Scikit-learn, NumPy, etc.
• Speech Technology: Strong working experience in building speech technologies, including Automatic Speech Recognition (ASR), Multilingual Speech Recognition, Diarization, Language Modelling, Pronunciation Modelling, Audio Classification
• Hands-on experience in building end-to-end speech recognition systems using modern architectures such as Whisper, wav2vec2, HuBERT, and LAS.
• Experience with speech processing frameworks like Kaldi, Espnet, Speech brain, etc.
• Machine Learning Frameworks:?Expert knowledge in at least one common ML framework (PyTorch or TensorFlow).
• Machine Learning Techniques:?Familiarity with basic ML techniques, including regression, classification, clustering, model metrics, neural networks, and performance evaluation.
• ML Pipelines:?Experience in developing and maintaining ML pipelines, including: Data preprocessing, Feature extraction, Model training, Model evaluation
• Deployment and Operations: Experience deploying complex ML algorithms to production in collaboration with engineers, using tools such as Flask and MLFlow.
• Strong debugging, problem-solving, and investigative skills. Ability to assimilate disparate information (e.g., log files, error messages) and pursue leads to identify root cause problems.
• Reviewing data science models and executing code refactoring, optimization, containerization, deployment, versioning, and continuous monitoring.
• Proficiency in ML model deployment, AI/ML pipeline development, and model monitoring.
• Participation in the development and maintenance of automated ML Ops pipelines.