DevOps Engineer

Key Profile -

Ambitious Project Manager with extensive experience in high-end Residential Interiors with demonstrated ability in planning, managing procurement, scheduling, quality control, and coordination of MEP services.

Job Profile -

• Manage Customer communication and Customer experience
• Delivery of Projects as per committed timeline & desired Quality
• To gather and share project information with Project Stakeholders
• Maintain strong relationships with Contractors, Suppliers, and Designers
• Ensure Execution at Sites as per Standard ensure execution process
• Willingness to learn new techniques for tracking projects
• Track & plan Site activities as per Master Project Schedule, identify the risk involved, and plan alternate on-site work execution strategies accordingly
• Verify the bills submitted by Contractors, suppliers post checking on-site measurements of work carried out at the site
• Coach & train Project Executives to ensure all Standard practices are getting followed as far as on-site execution and reporting are concerned.

Requirements

Desired Candidate Skills -

• Strong Communication Skills
• Technical Knowledge of High end Residential Interior Work
• Should be able to manage team of 3-4 people
• Knowledge of current market rates (Material & Labour rates) for various Residential Interior works

What you need to have:

• Must be either a Finance graduate / ICWA / MBA Finance / CA Inter.
• 5-7 years of relevant work experience in payroll function.
• Handled in and out payroll inputs, variance analysis and also understands drivers to payroll cost.
• Excellent communication skills and also with an excellent analytical mind.    

We are looking for an outstanding ML Architect (Deployments) with expertise in deploying Machine Learning solutions/models into production and scaling them to serve millions of customers. A candidate with an adaptable and productive working style which fits in a fast-moving environment.

Skills:

- 5+ years deploying Machine Learning pipelines in large enterprise production systems.

- Experience developing end to end ML solutions from business hypothesis to deployment / understanding the entirety of the ML development life cycle.
- Expert in modern software development practices; solid experience using source control management (CI/CD).
- Proficient in designing relevant architecture / microservices to fulfil application integration, model monitoring, training / re-training, model management, model deployment, model experimentation/development, alert mechanisms.
- Experience with public cloud platforms (Azure, AWS, GCP).
- Serverless services like lambda, azure functions, and/or cloud functions.
- Orchestration services like data factory, data pipeline, and/or data flow.
- Data science workbench/managed services like azure machine learning, sagemaker, and/or AI platform.
- Data warehouse services like snowflake, redshift, bigquery, azure sql dw, AWS Redshift.
- Distributed computing services like Pyspark, EMR, Databricks.
- Data storage services like cloud storage, S3, blob, S3 Glacier.
- Data visualization tools like Power BI, Tableau, Quicksight, and/or Qlik.
- Proven experience serving up predictive algorithms and analytics through batch and real-time APIs.
- Solid working experience with software engineers, data scientists, product owners, business analysts, project managers, and business stakeholders to design the holistic solution.
- Strong technical acumen around automated testing.
- Extensive background in statistical analysis and modeling (distributions, hypothesis testing, probability theory, etc.)
- Strong hands-on experience with statistical packages and ML libraries (e.g., Python scikit learn, Spark MLlib, etc.)
- Experience in effective data exploration and visualization (e.g., Excel, Power BI, Tableau, Qlik, etc.)
- Experience in developing and debugging in one or more of the languages Java, Python.
- Ability to work in cross functional teams.
- Apply Machine Learning techniques in production including, but not limited to, neuralnets, regression, decision trees, random forests, ensembles, SVM, Bayesian models, K-Means, etc.

Roles and Responsibilities:

Deploying ML models into production, and scaling them to serve millions of customers.

Technical solutioning skills with deep understanding of technical API integrations, AI / Data Science, BigData and public cloud architectures / deployments in a SaaS environment.

Strong stakeholder relationship management skills - able to influence and manage the expectations of senior executives.
Strong networking skills with the ability to build and maintain strong relationships with both business, operations and technology teams internally and externally.

Provide software design and programming support to projects.

 Qualifications & Experience:

Engineering and post graduate candidates, preferably in Computer Science, from premier institutions with proven work experience as a Machine Learning Architect (Deployments) or a similar role for 5-7 years.

Experience in Java (2+ years)

• Experience in SprintBoot(2+years)
• Good Knowledge of Microservice Concept
• Framework: Springboot, Spring Security, JAX-RS, Hystrix, Kafka
• ORM: Spring Data JPA. Hibernate
• Cloud Service: AWS(MSK, S3), Serverless lambda Functions
• Build tools: Maven, Gradle