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Engineer
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Tata Consultancy Services Limited
McLean, VA (In Person)
$110,000 Salary, Full-Time
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Job Description
Job Summary:
We are looking for motivated and enthusiastic trainees with a strong foundation in programming and emerging technologies. The ideal candidates should have hands-on knowledge of modern programming languages, familiarity with AI-enabled tools, and strong communication skills to effectively collaborate with teams and stakeholdersKey Responsibilities:
Develop and Enhance GenAI Solutions :
Build, fine-tune, and optimize LLM-based applications, ensuring high accuracy, reliability, and performance.Implement RAG and AI Pipelines :
Create end-to-end GenAI workflows using vector databases, embeddings, and cloud-based model orchestration.- Integrate AI into
Products :
Work with APIs, microservices, and backend systems to embed GenAI features into applications at scale.Monitor and Improve Model Performance :
Conduct evaluations, A/B tests, and model drift checks while applying prompt engineering and optimization techniques.Collaborate and Ensure Responsible AI :
Partner with cross-functional teams to implement secure, compliant, and ethically aligned AI systemsQualification and Specialization:
Required Skills & Qualifications:
- Design, build, and optimize GenAI solutions using LLMs, transformer-based models, and multimodal architectures for real-world business applications.
- Fine-tune and evaluate pre-trained models (e.g., GPT, Llama, Claude, Gemini) using domain-specific datasets to improve accuracy and performance.
- Develop scalable AI pipelines including data ingestion, model training, inference services, and deployment on cloud platforms (Azure/AWS/GCP).
- Implement Retrieval-Augmented Generation (RAG) using vector databases (Pinecone, FAISS, Chroma DB, Azure AI Search) to enable enterprise-grade QA and summarization systems.
- Integrate GenAI models into applications via APIs, SDKs, microservices, or custom backend frameworks (Python/Node.js).
- Optimize model performance through prompt engineering, model compression, quantization, and latency reduction techniques.
- Collaborate with cross-functional teams (data engineers, product owners, designers) to translate business needs into AI capabilities.
- Ensure security, compliance, and responsible AI practices including data privacy, model monitoring, bias mitigation, and ethical guidelines.
- Conduct experimentation and benchmarking to evaluate model performance, run A/B tests, and document results for continuous improvement.
- Stay up to date with the latest advancements in generative AI, LLM frameworks, and open-source tools while contributing to internal innovation initiatives.