Code Changes – clarifyall .com

public <T> T callJudiApi(String body, String url, Class<T> cls) {
    return webClient
            .post()
            .uri(url)
            .bodyValue(body)
            .retrieve()
            .onStatus(HttpStatusCode::is4xxClientError, this::handle4xxError)
            .onStatus(HttpStatusCode::is5xxServerError, this::handle5xxError)
            .bodyToMono(cls)
            .timeout(Duration.ofSeconds(timeoutInSeconds))
            .retryWhen(
                Retry.backoff(maxRetries, Duration.ofSeconds(timeGapBetweenRetriesInSeconds))
                    .filter(throwable -> throwable instanceof TimeoutException)
                    .doOnRetry(retrySignal -> 
                        log.warn("Retry attempt: {} due to error: {}", retrySignal.totalRetries(), retrySignal.failure()))
            )
            .onErrorResume(throwable -> {
                log.error("Error occurred during API call: {}", throwable.getMessage(), throwable);
                // You can add more custom error handling here like returning fallback data
                return Mono.error(new RuntimeException("Request Timeout"));
            })
            .doOnTerminate(() -> log.info("API call terminated."))
            .block();
}

public <T> T callJudiApi(String body, String url, Class<T> cls) {

return webClient

.post()

.uri(url)

.bodyValue(body)

.retrieve()

.onStatus(HttpStatusCode::is4xxClientError, this::handle4xxError)

.onStatus(HttpStatusCode::is5xxServerError, this::handle5xxError)

.bodyToMono(cls)

.timeout(Duration.ofSeconds(timeoutInSeconds))

.retryWhen(

Retry.backoff(maxRetries, Duration.ofSeconds(timeGapBetweenRetriesInSeconds))

.filter(throwable -> throwable instanceof TimeoutException)

.doOnRetry(retrySignal ->

log.warn("Retry attempt: {} due to error: {}", retrySignal.totalRetries(), retrySignal.failure()))

)

.onErrorResume(throwable -> {

log.error("Error occurred during API call: {}", throwable.getMessage(), throwable);

// You can add more custom error handling here like returning fallback data

return Mono.error(new RuntimeException("Request Timeout"));

})

.doOnTerminate(() -> log.info("API call terminated."))

.block();

}

Job Description – Gen AI Engineer

Position Overview

Role: Senior Gen AI Engineer

Responsibilities

Data Preparation: Collect, clean, and process data for training and evaluating multimodal foundation models, including generating synthetic data.
Model Development: Design and optimize large-scale language models such as GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders).
Language Tasks: Engage in language modeling, text generation, comprehension, and contextual understanding.
Model Tuning: Regularly review and refine Large Language Models to achieve maximum accuracy and relevance for custom datasets.
Application Deployment: Build and deploy AI applications on cloud platforms, including Azure, GCP, or AWS.
Integration: Enhance existing applications by integrating AI models with company data.

Role & Responsibilities

Data Handling: Manage data preprocessing, augmentation, and synthetic data generation.
Backend Development: Create and maintain backend services using Python or .NET to support OpenAI-powered solutions or other LLM frameworks.
AI Pipelines: Develop and maintain AI pipelines.
Dataset Utilization: Work with custom datasets, employing techniques like chunking and embeddings to train and fine-tune models.
Service Integration: Incorporate Azure cognitive services or similar platform services to extend functionality and enhance AI solutions.
Collaboration: Work with cross-functional teams to ensure effective deployment and integration of AI solutions.
System Robustness: Ensure the robustness, efficiency, and scalability of AI systems.
Continuous Learning: Stay abreast of the latest developments in AI and machine learning technologies.

Skills & Experience

Educational Background: Strong foundation in machine learning, deep learning, and computer science.
Generative AI Expertise: Proficient in generative AI models and techniques (e.g., GANs, VAEs, Transformers).
NLP and Computer Vision: Experience with natural language processing (NLP) and computer vision is advantageous.
Team Collaboration: Ability to work independently and collaboratively within a team.
Programming Knowledge: Proficient in advanced programming languages like Python and AI-focused libraries such as TensorFlow, PyTorch, and Keras, including implementing complex algorithms necessary for generative AI model development.
NLP Applications: Familiarity with NLP tasks for text generation, including text parsing, sentiment analysis, and using transformers like GPT (Generative Pre-trained Transformer) models.
Data Management: Experience in data management, including preprocessing, augmentation, and synthetic data generation, encompassing cleaning, labeling, and enhancing data to improve AI models.
Production Deployment: Experience in developing and deploying AI models in production environments.
Cloud Proficiency: Knowledge of cloud services (AWS, Azure, GCP) and understanding of containerization technologies like Docker and orchestration tools like Kubernetes for deploying, managing, and scaling AI solutions.
Innovative Mindset: Ability to contribute new ideas and innovative solutions for clients.

Job Details

Job Function: Application Development / Application Maintenance
Primary Location: Hyderabad, Telangana, India
Organization: IT Services – India
Employee Status: Full Time Employee
Schedule: Full-time
Job Type: Standard
Job Level: Professional
Shift: Day Job
Travel: No

Posting Information

Job Posting Date: January 6, 2025, 12:51:06 PM