Fulltime Opening for Full-Stack Engineer

Infojini Consulting is a full service IT consulting, services, and staffing firm with offices in Linthicum Heights ,Maryland, Washington, DC and Mumbai, India.

Infojini Consulting is recognized as one of the fastest growing IT services and software development Companies. With a partnership of all major technology vendors, Infojini Consulting has built a strong Government and commercial customer base including fortune 100 companies and most state and federal agencies such as State of North Carolina, State of South Carolina, State of Maryland, State of California, State of Pennsylvania, State of Virginia, State of Washington and many others.

Infojini Consulting is an equal opportunity employer and considers all qualified individuals for employment irrespective of their race, gender, age, color, sexual orientation. We offer an excellent compensation package

Job details mentioned below:

Job Title: Full-Stack Engineer

Location: San Francisco, CA

Duration: Full Time

Client: Direct Client

Experience:

Experience with medium-to-large scale RoR deployments

Strong fundamentals: OOP and application architecture

Appreciation for software craftsmanship, quality, maintainability and performance Attention to detail and personal pride in your work

EXTRA CREDIT:

Familiarity with system administration, database design, full-text search engines, machine learning

Front-end development with HTML, CSS, JS, jQuery, AngularJS, Bootstrap

Experience building APIs for web and mobile

Experience with scaling for exponential user growthExperience developing online marketplaces

Qualifications

AWESOME THINGS ABOUT THIS POSITION:

As engineer #6, you get to wear many hats. In addition to application development, we could use help with everything ranging from devops to machine-learning to map-making.

As a core member of a tiny company, you get to brainstorm with anyone, including the company founders, every day.Your features get used by millions of people. The feedback loop is short. You can tell, quantitatively, if your favorite feature is well received.

Additional Information