Role Proficiency:

Independently develops error free code with high quality validation of applications guides other developers and assists Lead 1 – Software Engineering

Outcomes:

     Understand and provide input to the application/feature/component designs; developing the same in accordance with user stories/requirements.      Code debug test document and communicate product/component/features at development stages.      Select appropriate technical options for development such as reusing improving or reconfiguration of existing components.      Optimise efficiency cost and quality by identifying opportunities for automation/process improvements and agile delivery models      Mentor Developer 1 – Software Engineering and Developer 2 – Software Engineering to effectively perform in their roles      Identify the problem patterns and improve the technical design of the application/system      Proactively identify issues/defects/flaws in module/requirement implementation       Assists Lead 1 – Software Engineering on Technical design. Review activities and begin demonstrating Lead 1 capabilities in making technical decisions

Measures of Outcomes:

     Adherence to engineering process and standards (coding standards)      Adherence to schedule / timelines      Adhere to SLAs where applicable      Number of defects post delivery      Number of non-compliance issues      Reduction of reoccurrence of known defects      Quick turnaround of production bugs      Meet the defined productivity standards for project      Number of reusable components created   Completion of applicable technical/domain certifications Completion of all mandatory training requirements

Outputs Expected:

Code:

Develop code independently for the above

Configure:

Implement and monitor configuration process

Test:

Create and review unit test cases
scenarios and execution

Domain relevance:

Develop features and components with good understanding of the business problem being addressed for the client

Manage Project:

Manage module level activities

Manage Defects:

Perform defect RCA and mitigation

Estimate:

Estimate time
effort
resource dependence for one's own work and others' work
including modules

Document:

Create documentation for own work as well as perform peer review of documentation of others' work

Manage knowledge:

Consume and contribute to project related documents
share point
libraries and client universities

Status Reporting:

Report status of tasks assigned Comply with project related reporting standards/process

Release:

Execute release process

Design:

LLD for multiple components

Mentoring:

Mentor juniors on the team Set FAST goals and provide feedback to FAST goals of mentees

Skill Examples:

Explain and communicate the design / development to the customer Perform and evaluate test results against product specifications Develop user interfaces business software components and embedded software components 5 Manage and guarantee high levels of cohesion and quality6 Use data models Estimate effort and resources required for developing / debugging features / components Perform and evaluate test in the customer or target environment Team Player Good written and verbal communication abilities Proactively ask for help and offer help

Knowledge Examples:

     Appropriate software programs / modules      Technical designing      Programming languages      DBMS      Operating Systems and software platforms      Integrated development environment (IDE)      Agile methods Knowledge of customer domain and sub domain where problem is solved

Additional Comments:

ho we are? You will be part of the ML R&D team which works on some really cool problems and (sometimes not-so-cool :-) problems). We apply cutting edge ML to solve hard problems like Document Understanding (or Document Al). We have a solution in production which is on par with the industry players in multiple facets. We reason things from the 1 st principles, or we build on top of existing things as the problem dictates. We as a team push the boundary of ML and constantly work on techniques to solve problems with no or little training data. We are a very flat org; everyone is technically sound and very collaborative. Your typical day would involve creating datasets from the scratch or run multiple iterations of feature engineering or come up with a great representation learning technique or conceptualize a nifty transfer learning solution, fit a model to the data and package the model to serve in batch or in online fashion. Who we are looking for? • We are flexible and are looking for the top talent ideally with 3-5 years industry experience or 1-2 years academic experience. • Programming Experience: Ninja Programmer in one of the following Python/ R. • Applied ML Experience: o Problem framing: ■ Strong problem framing skills: Say, when to go with Supervised or self-supervised or RL setting. o Data wrangling skills: ■ Experience in techniques like Weak/Distant Supervision and Pseudo labelling) ■ Strong EDA, data preparation and labelling skills ■ Strong data augmentation skills o From the scratch learning: ■ Strong experience in end to end modelling in (ML vs DL vs RL), ■ Experience in Single models vs Ensembles vs Mixture of experts. ■ Mathematical understanding of some Mathematical Induction, Tree Induction, DL and other optimization algorithms like SGD. o Transfer Learning ■ Experience in N-shot learning (or its variants) ■ Fine tuning skills UST Global Ltd 1 SmartOps Strategic R&D o ML/DL Verticals: Proven research or industry experience in one of the areas like Time series modelling, Vision, NLP, RL. • A GitHub portfolio with original ML repos. • A Kaggle portfolio with decent leader board positions • Papers: Original 1 st author papers in reputed ML journals or conferences. • Patents: Al or Automation specific patent is a good to have • Experience with ML/DL libraries TensorFlow or PyTorch • MLOps: Experience in running machine learning experiments with any one of the above machine learning libraries. Good to have is any one of the following: Kubeflow, Mlflow or Airflow or SparkML • Deploying machine learning solutions into production. Model Serving TFServe, Seldon, Custom serving. Interactive, batching and streamed serving. • Optimizing solutions for performance and scalability. • Data engineering, i.e. ensuring a good data flow between database and backend systems. • Implementing custom machine learning code (like custom implementation of existing algorithms like SGD) when required • Coming up with our own DNN architectures when required • Good to have: Computer science or IT background • Good to have: Exposure to statistics and probability. • Good to have: Experience in running dockerized code, we are a Kubernetes shop